Sequencing reads overlapping polymorphic sites in diploid mammalian genomes may be assigned to one allele or the other. This holds the potential to detect gene expression, chromatin modifications, DNA methylation or nuclear interactions in an allele-specific fashion. SNPsplit is an allele-specific alignment sorter designed to read files in SAM/BAM format and determine the allelic origin of reads or read-pairs that cover known single nucleotide polymorphic (SNP) positions. For this to work libraries must have been aligned to a genome in which all known SNP positions were masked with the ambiguity base 'N' and aligned using a suitable mapping program such as Bowtie2, TopHat, STAR, HISAT2, HiCUP or Bismark. SNPsplit also provides an automated solution to generate N-masked reference genomes for hybrid mouse strains based on the variant call information provided by the Mouse Genomes Project. The unique ability of SNPsplit to work with various different kinds of sequencing data including RNA-Seq, ChIP-Seq, Bisulfite-Seq or Hi-C opens new avenues for the integrative exploration of allele-specific data. Most functional NGS studies performed today still ignore the fact that many model organisms are diploid, and work on the averaged signal from the two alleles. However, a complete understanding of the biology of diploid organisms requires that the two alleles be measured separately. Allele-specific analysis of next-generation sequencing reads is becoming an important tool to identify events such as allele-specific expression of genes (ASE), allele-specific binding of transcription factors or histones (ASB) or allele-specific methylation (ASM). These techniques allow a more detailed investigation of the effects of genetic or epigenetic variation on genome regulation or studying parent of origin effects such as genomic imprinting or allelic imbalance.There are two main use cases for the investigation of allele-specific events: If both parental genotypes are clean and known in advance, e.g. for defined crosses of inbred mouse strains, parent of origin specific effects can be studied by comparing the two parental genotypes. Alternatively, allele-specific analyses require the more complex procedure of whole genome haplotype reconstruction , small insertions or deletions (InDels) or greater structural variations. While the allele-specific analysis of InDels has been found to be challenging3. Another approach involves the generation of two personalised genomes by incorporating known SNP positions (and possibly InDels) followed by an alignment to both genomes and finally a post-processing step to compute the union of the separate alignments , performing a single alignment to the N-masked genome and then assigning reads based on the sequence found underneath the masked positions. The rationale for N-masking in allele-specific alignments is that the mapping bias towards the reference allele is eliminated and both alleles of the same read get placed in the same position in the genome equally well. N-masking the genome is a one-off exercise and this approach has the advantage of requiring only a single alignment to a reference which noticeably reduces the computational load. Despite the fact that N-masking effectively avoids allelic biases it may occasionally result in a minor loss of sensitivity when the density of N covered by a read is getting too high.Several approaches have been taken to perform allele-specific alignments. The simplest is to align all reads to a single reference genome, but this introduces a bias as reads from the allele which is more similar to the reference are able to map more efficiently8 (hosted athttp://www.sanger.ac.uk/science/data/mouse-genomes-project). If the genotype is not known, SNP positions may be called from the data itself, or from genome re-sequencing performed in parallel. The quality of the genotype calls is crucial for allele assignment, so the genotype data needs to be collected carefully and quality control and filtering is required to avoid biases and false positive hits9. Further downstream analysis of allele-specific data is highly dependent on the experiment type and is beyond the scope of this manuscript.A requirement for N-masking is that SNP positions are known, e.g. via a public resource like the Mouse Genomes Project which provides high quality variant calls for a large number of mouse strains10, the unique capability to work with several different data types renders SNPsplit an ideal choice for correlation studies using allele-specific sequencing reads.To our knowledge there are currently no user-friendly solutions available for the allele-specific splitting of sequencing reads aligned to N-masked genomes. We sought to address this by creating SNPsplit, an easy-to-use tool for assigning allele-specifc reads. In its generic mode SNPsplit is not tied to any particular aligner and operates across several different experiment types including RNA-Seq, genomic DNA-alignments, DNA methylation (Bisulfite-Seq) and 3-D genome organisation (Hi-C). While a similar allele-specific functionality has been integrated into specialised applications, e.g. HiC-ProSNPsplit is written in Perl and consists of three separate scripts that can be run individually on the command line. It takes alignment files in BAM/SAM format as input and further requires an annotation file containing the positions of all SNPs in the genome. SNPsplit determines for each aligned read whether it overlaps with a known SNP position and adds a tag to the alignment that indicates whether the read can be assigned to a specific allele or is unassignable. The reads are then sorted into different sub-files depending on the library type, i.e. single-end or paired-end, and the nature of the sample, e.g. RNA-Seq, BS-Seq or Hi-C.As long as a SAM/BAM file that was aligned to an N-masked genome is provided as input SNPsplit should perform well regardless of how the N-masking itself was accomplished. Since there is an ever growing number of genomes and different SNP annotation files and file formats it would be too much to ask to provide a generally applicable way of constructing N-masked genomes that fits all cases.http://www.sanger.ac.uk/science/data/mouse-genomes-project). The genome preparation step supports the generation of single hybrid strains where one allele is the same as the mouse reference sequence  and one alternative allele, e.g. SPRET/EiJ. It also supports the generation of dual hybrid strains where both alleles are different from the Black 6 reference, e.g. CAST/EiJ and 129S1/SvImJ. At the time of writing the Mouse Genomes Project encompassed variation information for 36 different mouse strains; the SNP annotation data for all strains relative to Black 6 reference sequence may be found in the variant call format (VCF) file ‘mgp.v5.merged.snps_all.dbSNP142.vcf.gz’ . The SNPsplit genome preparation first reads the SNP annotations for the strain in question from the VCF file and then constructs the N-masked genomes based on the Black 6 reference sequence using only high confidence homozygous positions. The process is slightly different for single or dual hybrid strains  is the Black 6 reference and Allele 2 (or Genome 2) is the alternative strain.1) The VCF file is read and filtered for high-confidence SNPs for the strain specified2) The Black 6 reference genome is read into memory, and the filtered high-confidence SNP positions are incorporated either as N-masking (default) or full sequence Dual-Hybrid Strains. This generates a new genome sequence where neither allele is the Black 6 reference. SNPs can be either N-masked or included as full sequence, where Allele 1 (or Genome 1) is the strain specified as strain 1 and Allele 2 (or Genome 2) is the strain specified as strain 2.1) The VCF file is read and filtered for high-confidence SNPs in strain 12) The Black 6 reference genome is read into memory, and the filtered high-confidence SNP positions are incorporated as full sequence and N-masking 3) The VCF file is read and filtered for high-confidence SNPs in strain 24) The filtered high-confidence SNP positions of strain 2 are incorporated as full sequence and N-masking 5) The SNP information of strain 1 and strain 2 relative to the Black 6 reference genome build are compared and a new Ref/SNP annotation is constructed whereby the new Ref/SNP information will be strain 1/strain 26) The full genome sequence of strain 1 is read into memory, and the high-confidence SNP positions between strain 1 and strain 2 are incorporated as full sequence and N-masking 11, BWA12, HISAT213, STAR14 or any tool wrapping one of these aligners.The N-masked sequences (or sequences containing the full sequence SNPs) are written out in FASTA format and ready to be indexed with the alignment software of your choice. Alignments to N-masked genomes are not very different to regular mapping except that they require the aligner to support ambiguity DNA bases such as N. Software confirmed to be working for this approach include (but are not limited to) Bowtie2Even though the automated genome preparation is optimised to work with the VCF file from the Mouse Genomes Project the process can be easily adapted to work with any other genome as long as the genotypes are known and well defined. The SNPsplit manual provides more detailed information about the SNP filtering from VCF files and which entries are required to make it work also with other genomes.SNPsplit operates in two stages which are run sequentially: I) read tagging and II) read sorting. Both steps generate detailed reports for record keeping.Stage I: Tagging SNPsplit analyses reads for overlaps with known SNP positions for which it requires the mismatch position field (MD:Z:) in the SAM entry, and writes out a tagged BAM file in the same order as the original file. This process requires a list of all known SNP positions between the two different genomes (supplied as a SNP file) and works on a read-by-read basis.Read tagging generally works as a multi-step process:1. Determine the position(s) in the read that overlap genomic N(s)2. Adjusting position for insertions/deletions3. Determine equivalent genomic position4. Determine if the SNP is present in the list of SNP positions, and if yes whether the position in the read was the Allele 1 or Allele 2 baseDepending on the collected SNP information the tagging module then determines whether a read can be assigned to a certain allele and appends an additional optional field ‘XX:Z:tag’ to the SAM entry of each read. The tag can be one of the following:UA - UnassignedG1 - Genome 1-specific G2 - Genome 2-specific CF - ConflictingReads are considered unassignable (UA) if they do not overlap any known SNP position. Reads harbouring at least one SNP specific for both genomes at the same time are classified as conflicting (CF).15) are currently not supported. This means that SNPsplit requires reads to be a full match from end-to-end and thus soft-clipping (CIGAR operation: S), which may introduce artefactual alignments to poorly annotated regions in the genome16 is not supported .The determination of overlaps is geared to handle the CIGAR operations M (match to the reference), D (deletion in the read), I (insertion in the read) and N . Other CIGAR operations . Conflicting reads, or also disagreeing read-pairs for paired-end samples, are not printed out by default. The sorting process may also be run stand-alone on tagged BAM files to try out different sorting options .15 (v0.1.18 or later) is required for handling of SAM/BAM files. Memory requirements depend directly on the genome size and the total number of heterozygous SNPs to be stored, but as a guideline 5–10 GB RAM should be sufficient to process data for most mouse strains.SNPsplit runs on any Linux-based operating system with Perl installed (tested using CentOS v6.2 and Perl v5.10.1). In addition, a functional version of SAMtoolsSNPsplit is able to handle any kind of standard genomic alignment file irrespective of the method employed to generate the library as long as the CIGAR operation requirements are met (see Stage I: Tagging above). A non-exhaustive list of supported applications includes genome re-sequencing, histone or protein ChIP-Seq (chromatin immunoprecipitation sequencing) or ATAC-Seq (Assay for Transposase-Accessible Chromatin by sequencing).A use case of ChIP-Seq for the transcription factor ZFP57 is shown in18, STAR14 (Spliced Transcripts Alignment to a Reference) and HISAT213. To work smoothly together with SNPsplit, HISAT2 and STAR require the user to disable soft-clipping which is performed by default (CIGAR operation: S), and STAR also needs to be instructed to print out the mismatch position (MD:Z:) field. More detailed instructions may be found in the SNPsplit User Guide.In addition to standard linear alignments with or without small InDels, SNPsplit also handles spliced read alignments containing large gaps (CIGAR operation: N), such as reads spanning exon boundaries in RNA-Seq experiments. Spliced read aligners that have successfully been used for allele-specific alignments in conjunction with SNPsplit include Tophat19. This is accomplished by measuring the frequency at which different parts of the genome sequence come into close physical contact. While standard Hi-C cannot discriminate whether an interacting fragment originated from the same or the other allele, allele-specific interaction maps can separate cis-allele from trans-allele interactions, thereby greatly improving the analysis of chromatin dynamics and gene regulation21.As a variant of the chromatin conformation capture assay Hi-C is a proximity-ligation based assay which allows the investigation of the three-dimensional structure of the genome by massively parallel sequencing22, i.e. the input BAM files are by definition paired-end and Read 1 and Read 2 follow each other. It discriminates several additional read combinations to distinguish between cis- and trans-allele interactions:The Hi-C mode of SNPsplit assumes that the input data is in the Hi-C format produced by the HiCUP pipeline- G1-G1- G2-G2- G1-UA- G2-UA- G1-G2- UA-UAFor mixed allele groups such as G1-G2 there is no need to create the reverse group (G2-G1) since Hi-C interactions have no directionality. Again, read pairs containing at least one conflicting read (tag: CF) are not printed out by default, but this may be optionally enabled.Bisulfite sequencing is a method to interrogate DNA methylation patterns using the chemical properties of sodium bisulfite to convert cytosines to uracil but leaving methylated cytosines largely unaffected.23. SNPsplit runs a quick check at the start of a run to see if the file provided appears to be a Bismark file, and sets the appropriate flags for bisulfite and/or paired mode automatically. Paired-end mode requires Read 1 and Read 2 of a pair to follow each other in consecutive lines so the BAM file will be sorted by read name if necessary.The bisulfite mode of SNPsplit assumes that the input data has been processed with the bisulfite alignment tool BismarkUtilisation of SNP positions and allele assignment of bisulfite treated reads In contrast to the standard mode, C>T SNPs may not always be used for allele-specific sorting in a bisulfite setting since they could either be a genuine SNP or rather reflect the methylation state. Since the majority of known SNPs actually involves C to T transitions (due to spontaneous deamination of methylated CpG dinucleotides), the ability to assign aligned bisulfite treated reads is thus somewhat reduced compared to regular DNA-based alignments. The number of SNP positions that have been skipped because of this bisulfite ambiguity is documented in the report file.Positions requiring special treatment include all of the following Allele 1/Allele 2 combinations: C/T or T/C for forward strand alignments and G/A or A/G for reverse strand alignments. These positions may however be used to assign opposing strand alignments since they do not involve C to T transitions directly. For that reason, the bisulfite call processing also extracts the bisulfite strand information from the alignments in addition to the basecall at the position involved. For any SNPs involving C positions that are not C to T SNPs both methylation states, i.e. C and T, are allowed to match the C position.For SNPs which were masked by Ns in the genome no methylation call will have been performed during the alignment step, i.e. they will receive a ‘.’ (dot) in the methylation call string. This means that SNP positions themselves may be used for allele-sorting but do not participate in calling methylation. While this reduces slightly the number of total methylation calls it effectively eliminates the problem of assigning potentially incorrect methylation states to these positions.6 (GEO accession: GSE33722). First we generated a dual hybrid genome for 129X1/SvJ (129) (as near-enough relative we used the SNP annotations for strain 129S1/SvImJ) and Cast/EiJ (Cast) mice, and then aligned the data to the N-masked genome using Bismark . The data was then processed with SNPsplit and all datasets for the F1 forward cross 129 (mother) x Cast (father), and F1 reverse cross Cast (mother) x 129 (father) were merged and analysed using SeqMonk (v.0.33.025)  .Whilst the majority of the genome shows very similar methylation levels on both alleles of the hybrid mice, this approach also allows the detection of allele-specific methylation events. This can be readily spotted at imprinted loci where one parental allele is fully methylated while the other remains completely unmethylated. The Gnas/Nespas locus in the mouse genome shows both a paternally methylated region (more upstream) and maternally methylated region (more downstream) where the allele-specific methylation pattern is maintained in a parent-of-origin dependent manner . This deAnalysing next-generation sequencing data in an allele-specific fashion holds the potential to uncover regulatory events or mechanisms that would otherwise be obscured in bulk data. SNPsplit is designed to enable researchers to quickly and easily perform allele-specific analysis of their sequencing data as long as the SNP genotypes of the organism in question are known. For hybrid mouse strains covered by the Mouse Genomes Project, SNPsplit offers an easy solution from generating N-masked genomes to allele-specific sorting of reads without requiring the user to possess excessive computational skills. SNPsplit is not tied to any specific application and indeed it has been used already to answer questions for a variety of different data types such as ChIP-Seq, RNA-Seq, Bisulfite-Seq and Hi-C. This gives SNPsplit the unique capability of bringing together allele-specific data including gene-expression, DNA methylation, genomic accessibility or architecture which holds great potential for studying genome regulation.1. http://www.bioinformatics.babraham.ac.uk/projects/SNPsplit/Software available from:2. https://github.com/FelixKrueger/SNPsplitLatest source code:3. https://zenodo.org/record/55477#.V18PoDb93ww26Archived source code as at time of publication:4. Software license: GNU GPL v3 or later I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. et al. describes in detail a new software SNPsplit which is capable of sorting reads or read–pairs in the allele specific modus that covers know single nucleotide polymorphic (SNP), or single nucleotide variation (SNV) locations. For sorting the reads the inputs to the software are a ‘N’ masked genome, created using known SNP or SNV locations, to which the reads are aligned using any available aligner and a VCF file containing the positions of all SNPs. The authors claim that this is the only user-friendly solution available for the allele-specific splitting of sequencing reads aligned to N-masked genomes. Overall the manuscript is very well written. However, I have some minor queries regarding the software performance:Masking known SNP positions in the genome sequence eliminated the reference bias but, in case of heterozygous SNPs there could be a chance of having significant bias toward higher mapping rates of the allele in the reference sequence, is there any provision in the software to remove this noise and bias from mapped reads? This kind of bias can lead to false signal of allelic imbalance. It is stated that the software can construct the N-masked genomes, so is it restricted to mouse alone or can be extended in case of humans or other species?Can SNPsplit be used to split reads for indels and deletions? I have personally used the software for ATAC-seq data from patients and it works perfectly fine. I have not used the genome builder module of the software. The data looks perfect in the UCSC genome browser for the ‘Allele 1’ or ‘Allele 2’ and the unassigned. Overall I felt it is a very user friendly software available.This article by Krueger FI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. We would like to thank the reviewer for their kind and approving comments about SNPsplit. Some specific comments may be found below.Masking known SNP positions in the genome sequence eliminated the reference bias but, in case of heterozygous SNPs there could be a chance of having significant bias toward higher mapping rates of the allele in the reference sequence, is there any provision in the software to remove this noise and bias from mapped reads? This kind of bias can lead to false signal of allelic imbalance. SNPsplit is really designed for mapping against genomes with clean parental genotypes; this is also why the genome preparation step is very strict and only uses high quality homozygous parental SNPs . If the SNPs are heterozygous already in one or even both of the parental strains we propose that these positions could be masked by Ns for the mapping step, but not be used at all for the allele-specific read assignment as such. For the allele-specific splitting, i.e. the file supplied to SNPsplit with --snp_file, only homozygous high quality SNP positions should be used.It is stated that the software can construct the N-masked genomes, so is it restricted to mouse alone or can be extended in case of humans or other species? The SNPsplit approach does in theory work on any N-masked genome irrespective of the species. The preparation is currently optimised to work the VCF file provided by the Mouse Genomes Project but as long as the VCF file conforms to the same standards it should also work for other genomes. If the VCF file looks different, e.g. you got the file from a collaborator and it came without header lines or format description one would have to adapt the relevant section(s) in the preparation script. We have now added a section to the SNPsplit User Guide that explains in more detail the basis of SNP filtering and which parameters are needed to run properly. We hope that this will help generating N-masked genomes when the SNP data looks different than the file provided by Mouse Genomes Project.Can SNPsplit be used to split reads for indels and deletions? SNPsplit supports the processing of reads that contain indels, but indels themselves are not used to sort reads to different genomes at the current time. The reason for this is mainly that indel annotations are often more tricky to come by and alignments over indels (e.g. at the ends of reads) are notoriously more difficult. There are currently no plans to extend the functionality to include indels, but we might look into this in the future if the demand arises. This manuscript by Krueger and collaborators describes SNPsplit, an alignment sorter for allele specific analysis. SNPsplit is designed to work on N-masked alignment and provides additional utilities to generate the appropriate reference. The main interest of SNPsplit is its ability to operate across different experiment types and alignment software. It therefore allows to analyse and to integrate in an allele specific and unbiased manner heterogeneous dataset. The manuscript is well written, and is divided in two main parts. The first part presents the software and its implementation and the second part, presents user cases.The way the N-masked genome is generated based on SNPs information is not that easy to understand for non expert users. I would recommend a figure to help in understanding this point. In the context of Dual-Hybrid strain, the interest of first generating the strain 1 (S1) and the strain 2 (S2) genomes is not clear to me. If I'm correct, the final masked genome will be generated from the S1 fasta reference only (unmasked). If we use a simple example with a SNP which is Bl6=A, S1=T, S2=T, using this strategy will avoid a mismatch at this position, compared to the strategy of masking only heterozygous SNPs between S1/S2 on the reference (Bl6) genome. But what is the interest of generating the S2 reference genome (step 4) ? Using S1 or S2 should be enough?How paired-end sequencing is managed in practice ? The authors present an application with Hi-C data but is it the same for any NGS application?The authors present SNPsplit in the context of the Mouse Genome Project. I'm wondering how difficult it would be to transpose it to any genome and organism as long as the genotype is known. A few words about that in the manuscript would be interesting.on the reference genome, then realigning reads that overlap SNP positions in all possible allele combinations and keeping only reads that align to the same positionregardless their genotypes - this reduces bias, but is computationally complex”In the Introduction, the authors mentioned the WASP software[7]. To avoid any misunderstanding with the parental genome strategy, I would suggest something like; “A more recent improvementaims to reduce mapping biases by first aligning readsAt the end of the introduction, the authors mentioned that “SNPsplit is not tied to any particular aligner” which is not exactly true as the authors explain later that “SNPsplit can be used as long as the CIGAR operation requirements are met”Typo:Stage I: Tagging “The determination of overlaps is geared” in Reference 26 and 27 doesn't seem to be used in the text Other than my few comments below, I am happy with the manuscript and want to note that I have successfully downloaded and used SNPsplit in the context of several projects.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Many thanks for the constructive review and the thoughtful suggestions on how to improve the manuscript. Please find our point-by-point responses below.The way the N-masked genome is generated based on SNPs information is not that easy to understand for non expert users. I would recommend a figure to help in understanding this point. We have now added a new Figure 1 that aims to explain the process of generating single or dual hybrid N-masked genomes in more detail. We feel that this new figure makes the whole process substantially easier to understand, many thanks for suggestion!In the context of Dual-Hybrid strain, the interest of first generating the strain 1 (S1) and the strain 2 (S2) genomes is not clear to me.If I'm correct, the final masked genome will be generated from the S1 fasta reference only (unmasked). If we use a simple example with a SNP which is Bl6=A, S1=T, S2=T, using this strategy will avoid a mismatch at this position, compared to the strategy of masking only heterozygous SNPs between S1/S2 on the reference (Bl6) genome. But what is the interest of generating the S2 reference genome (step 4) ? Using S1 or S2 should be enough? Technically it would be sufficient to use the Strain 1 unmasked genome as new reference and then use the SNP annotations of both strains to compute a new Strain1/Strain2 SNP annotation file . Since writing out a new genome only takes a few seconds and it might be of potential use for other projects we do also write out versions of Strain 2, it can always be removed later if desired.How paired-end sequencing is managed in practice? The authors present an application with Hi-C data but is it the same for any NGS application? We do mention in the manuscript that the paired-end mode uses both ends for the genome-specific assignments even though it is not as complicated as the Hi-C mode. The SNPsplit User Guide already contains more detailed information about paired-end file handling and also provided paired-end sorting reports to illustrate this, so we would kindly refer the user to the manual rather than adding an extra section here.The authors present SNPsplit in the context of the Mouse Genome Project. I'm wondering how difficult it would be to transpose it to any genome and organism as long as the genotype is known. A few words about that in the manuscript would be interesting. Indeed, as long as the genotypes are well defined and the list of SNP positions is known the genome preparation should also work well with any other genome . To enable this process we have added a new section to the SNPsplit User Guide that explains the SNP filtering and processing in more detail, and we have added a short paragraph about this to the manuscript.In the Introduction, the authors mentioned the WASP software[7]. To avoid any misunderstanding with the parental genome strategy, I would suggest something like; “A more recent improvementon the reference genome, then realigning reads that overlap SNP positions in all possible allele combinations and keeping only reads that align to the same positionregardless their genotypes - this reduces bias, but is computationally complex”aims to reduce mapping biases by first aligning reads We have changed this sentence to make it clearer.At the end of the introduction, the authors mentioned that “SNPsplit is not tied to any particular aligner” which is not exactly true as the authors explain later that “SNPsplit can be used as long as the CIGAR operation requirements are met” At least in its generic mode SNPsplit is not tied to any specific aligner, but this does not necessarily mean that every single option of any aligner will be supported. To this end, SNPsplit was initially designed to work with Bowtie2 and Tophat, but we have later also seen other software such as STAR or HISAT2 work fine as well. Aligner-specific comments can be found in the SNPsplit User guide.Reference 26 and 27 doesn't seem to be used in the text Thanks for spotting this, we have now removed these references from the bibliography. Krueger and Andrews report SNPsplit, a tool for sorting mapped next generation sequencing reads into separate files depending on the allelic origin of the read. The process requires genetic heterogeneity between the alleles such that the reads can be assigned to a particular allele based on known SNP positions. Such a tool allows for allele specific analysis and becomes useful for studies on phenomena such as genomic imprinting, allelic imbalance and X-chromosome inactivation. SNPsplit improves on current methods for analysis of allele specific sequencing reads by providing built in tools for the analysis of bisulfite and HiC data, which are otherwise more complicated to analyse. A tool for creating an N-masked genome is also provided, which overcomes mapping bias towards the reference genome. To date I have used SNPsplit to process data from RNA-seq, bisulfite-seq, transcription factor ChIP-seq and histone ChIP-seq. I have not tested the built in N-masked genome creator. SNPsplit has proven to be easy to use and very stable in my run environment. Typically, a sample is processed in approximately 3 hours depending on read depth. As far as I can tell, by assessing known imprinted genes and the silent female X chromosome, SNPsplit does an excellent job of assigning reads to the correct genome, with known phenomena appearing as expected. The built in option for analysis of bisulfite data works very well on reads mapped with the bismark program. I’ve found SNPsplit to work very well for all the data types I have used it for: RNA-seq, bisulfite-seq, transcription factor ChIP-seq and histone ChIP-seq. It should be noted however that due to the requirement for a SNP to be present in the read, assignable reads from narrow transcription factor ChIP-seq peaks can be sparse and some peaks may not assignable at all. This is an unavoidable limitation, and the authors show successful assignment of transcription factor ChIP-seq reads in Figure 2, however this is perhaps something researchers should consider. Reads deriving from broad histone ChIP-seq peaks suffer no such limitation. I have no issues with the performance of SNPsplit, and neither the accuracy of how the tool is presented in the paper. Perhaps the authors could include some details that will aid researchers in experimental design?  For example, for a typical SNP density what percentage of reads would be assigned to each genome, unassigned and conflicting? This would help researchers in estimating required read depths for their particular question. Could the authors also explain how vcf files are filtered for high confidence SNPs when preparing the N-masked genome?  A discussion of post processing techniques for the removal of incorrectly annotated SNPs would also be beneficial. Perhaps the benefit of longer read length for assigning reads to a particular genome could also be mentioned? In summary, SNPsplit will prove to be a very useful tool for the analysis of epigenomic data from next generation sequencing experiments.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. We would like to thank the reviewer for their kind and positive feedback. Please find our specific replies below.Could the authors also explain how vcf files are filtered for high confidence SNPs when preparing the N-masked genome?   We have expanded the SNPsplit User Guide considerably to now include a detailed section on how the SNP filtering is accomplished and which of the parameters are required for the process to work with other VCF files. We hope that this will make the process easier to understand and allow adapting the procedure for other genomes as well.Perhaps the authors could include some details that will aid researchers in experimental design?  For example, for a typical SNP density what percentage of reads would be assigned to each genome, unassigned and conflicting? This would help researchers in estimating required read depths for their particular question. A discussion of post processing techniques for the removal of incorrectly annotated SNPs would also be beneficial. Perhaps the benefit of longer read length for assigning reads to a particular genome could also be mentioned? It would be undoubtedly helpful to provide some more guidance about experimental design, however we are not sure that this should be added in the context of software manuscript. As a general guideline the percentage of reads that can be assigned allele specifically increases proportionally with the number of heterozygous SNPs present between two strains, and increasing the read length also increases the chances to hit a SNP. Furthermore paired-end data can be assigned with a much rate than single-end reads. The SNPsplit User Guide contains a few example reports to help users to get an idea about typical values. We also feel that post-processing techniques probably warrant more discussion than just being briefly mentioned here, a good paper to read to get started is also cited in the manuscript .


Next-generation genotyping microarrays have been designed with insights from large-scale sequencing of exomes and whole genomes. The exome genotyping arrays promise to query the functional regions of the human genome at a fraction of the sequencing cost, thus allowing large number of samples to be genotyped. However, two pertinent questions exist: firstly, how representative is the content of the exome chip for populations not involved in the design of the chip; secondly, can the content of the exome chip be imputed with the reference data from the 1000 Genomes Project (1KGP). By deep whole-genome sequencing two Asian populations that are not part of the 1KGP, comprising 96 Southeast Asian Malays and 36 South Asian Indians for which the same samples have also been genotyped on both the Illumina 2.5 M and exome microarrays, we discovered the exome chip is a poor representation of exonic content in our two populations. However, up to 94.1% of the variants on the exome chip that are polymorphic in our populations can be confidently imputed with existing non-exome-centric microarrays using the 1KGP panel. The coverage further increases if there exists population-specific reference data from whole-genome sequencing. There is thus limited gain in using the exome chip for populations not involved in the microarray design. Instead, for the same cost of genotyping 2,000 samples on the exome chip, performing whole-genome sequencing of at least 35 samples in that population to complement the 1KGP may yield a higher coverage of the exonic content from imputation instead. Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) that are associated with common diseases and complex traits While exome sequencing provides an unbiased survey of exonic variants, the cost is still prohibitive to hundreds or thousands of samples for an association study. Next-generation genotyping microarrays designed with information from population-level whole-genome and whole-exome sequencing provide a well-intentioned compromise for surveying the genome with a genotyping approach at a fraction of the cost. For example, the Illumina HumanExome array included coding variants that were consistently identified across multiple individuals from a collection of around 12,000 subjects from diverse populations with African, Chinese, European and Hispanic ancestries, and this microarray was competitively priced with the intention of extending its use to existing GWAS cohorts. However, one of the challenges of working with exonic variants at the rarer end of the allele frequency spectrum is the greater tendency for these variants to be population specific Statistical imputation has been widely employed in GWAS for inferring the genotypes at SNPs that are not available on the microarrays but are present in population-level haplotype reference data Here, we aim to address the following questions: (i) to what extent does the Illumina Exome chip represent the exonic content of two Asian populations that are not involved in the microarray design? (ii) for studies where there are pre-existing GWAS data, to what extent can the exonic content of these populations be recovered accurately through the process of statistical imputation against the reference map from Phase 1 of the 1KGP, thereby removing the need for additional genotyping on the exome chip? (iii) does having population-specific haplotype maps on top of the 1KGP reference maps help in recovering the exonic variants that are present in these populations? To answer these questions, we utilized the resources from: (a) the Singapore Integrative Omics Study (iOmics), where each of 110 southern Han Chinese, 108 Southeast Asian Malays and 105 south Asian Indians in Singapore has been genotyped on both the Illumina HumanOmni2.5 and Illumina HumanExome microarrays; (b) the Singapore Sequencing Study, where 96 Singapore Malays and 36 Singapore Indians from the iOmics have been whole-genome sequenced to a target coverage of 30-fold; and (c) the haplotype reference maps from Phase 1 of the 1KGP. By down-sampling the variants on the HumanOmni2.5, we recreated the SNP content on earlier generations of microarrays such as the HumanHap550 and Human1M for evaluating how well these commonly-used products can recover the content of the exome chip.http://www.statgen.nus.edu.sg.The Singapore Integrative Omics Study (iOmics) surveyed 120 individuals from each of three populations in Singapore consisting of southern Han Chinese, Southeast Asian Malays and south Asian Indians. These individuals were recruited from the Singapore Population Health Study, and population membership of each subject was determined through self-reports that all four grandparents belong to the same population. Each of the 360 subjects was genotyped on both the Illumina HumanOmni2.5 and HumanExome microarrays. A subset of these samples were additionally whole-genome sequenced to a target coverage of 30-fold. These subset of samples include 62 of the Malays and 36 of the Indians. Note that subsequent assessment of imputation coverage and accuracy was restricted to the subset of Malay and Indian subjects that have not been sequenced in order to avoid over-fitting. All participants provided written informed consent, and ethical approvals for the Singapore Population Health Study and the subsequent extension to the iOmics were granted from two independent Institutional Review Boards at the National University Hospital Singapore and the National University of Singapore respectively. In particular, the consent form was approved by the Institutional Review Boards prior to the commencement of the sample recruitment. The data for both the microarray genotyping and whole-genome sequencing can be accessed publicly at −8); (B) a sample QC step performed across all 360 samples jointly to identify problematic samples, defined as samples (i) with high missingness (>2%), (ii) with excessive identify-by-state (IBS) genotypes (see Table S1), or (iii) that were genetically inferred to be outliers with principal components analysis (PCA) from the self-reported population membership; (C) a final round of SNP QC performed for each population to exclude SNPs with more than 5% missing genotypes or with HWE p-value <0.001. This yields a final set of 110 Chinese, 108 Malay and 105 Indian subjects with post-QC data for both the Omni2.5 and exome chip, with a similar number of SNPs that passed QC in the three populations (see Table S2).Quality control (QC) of the two sets of genetic data  was performed independently and in the following three steps: (A) a preliminary QC step performed across all 360 samples jointly to generate a set of pseudo-clean SNPs by removing SNPs (i) with either unknown or duplicate genomic coordinates, (ii) not in the autosomal and X/Y chromosomes, (iii) with unknown strand information, (iv) with conflicting allele designation between the two microarrays, (v) that occur on both microarrays but with genotype concordance <99.5%, (vi) with missingness >5%, or (vii) with gross departure from Hardy-Weinberg equilibrium  are obtained from the covariance matrix. We run PCA on two set of data, i.e. with the HapMap data which consists of 1396 individuals from different populations and with only the three Singapore population groups. Samples that displayed evidence of admixture or discordance between self-reported and genetically inferred population membership are identified and excluded from the study.r2  = 1) with the target SNP in both CEU and JPT+CHB; (ii) is in perfect correlation with the target SNP in only CEU; (iii) is in perfect correlation with the target SNP in only JPT+CHB; (iv) exhibits the highest r2 with the target SNP in the combined CEU and JPT+CHB dataset, provided the highest r2 is at least 0.80. The search for surrogates considered SNPs on Omni2.5 that were located within 1 Mb of each target SNP. This procedure allowed us to recover at least 470,208 SNPs and 803,333 SNPs on the HumanHap550 and Human1M respectively (see Table S3).As the iOmics samples have only been genotyped on the Omni2.5 and exome chip, subsets of the SNPs on Omni2.5 were taken to mimic the content of the Illumina HumanHap550 and Illumina Human1M microarrays. Given that only 377,563 SNPs and 681,328 SNPs on the Omni2.5 overlapped with the contents of the HumanHap550 and Human1M respectively, we searched for surrogates that existed on the Omni2.5 to recover the effective coverage of the latter two microarrays. This is achieved by considering the CEU and JPT+CHB resource from the 1000 Genomes Project, where to identify a surrogate for a target SNP, we consider the following situations in a hierarchical fashion to locate a SNP on Omni2.5 that: (i) is in perfect correlation (S6); (ii) complementing the 1KGP reference panel with the reference panel from the Singapore Sequencing Malay Project (SSMP) – a population-level deep whole genome sequencing of 96 Southeast Asian Malays in Singapore The microarray data from the samples were pre-phased using SHAPEIT v2.r644 To assess whether statistical imputation can successfully recover the unobserved exonic SNPs effectively, we measured the quality of the imputed data at the SNP level and at the individual sample genotype level.At the SNP level, we considered the information criterion generated by IMPUTE (info) as part of the imputation process. This metric is meant as a measure of the multi-SNP correlation between neighboring SNPs and the target SNP. We defined a SNP to be well-imputed when info was at least 0.3. In addition, we required the imputation to produce high-confidence genotypes for the samples at each SNP. We measured this by calculating the proportion of the samples that carried a valid genotype call, defined as an imputed genotype possessing a posterior probability that was at least 0.90. When all three genotype classes possessed posterior probabilities less than 0.90, a missing genotype was assigned. An imputed SNP was considered to produce high-confidence genotypes when at least 95% of the samples were assigned valid genotypes. Thus a SNP was deemed to be poorly imputed if either info <0.3 or call rate <0.95.S9 for Omni2.5, S12 for HumanHap550, S15 for Human1M on the number of high-confidence imputed SNPs). The motivation behind measuring minor allele concordance is due to the greater emphasis of getting such genotype calls to be accurate for the intended purpose of the exome chip – to locate low-frequency or rare coding SNPs that are associated with phenotypic outcomes.As the info score may not necessarily be an accurate measurement of imputation performance for SNPs with low minor allele frequency, and to capture the situations where the imputation algorithm incorrectly produced high-confidence or high-information output due to the use of an inappropriate reference panel for the target population, we also considered the accuracy of the imputed genotypes by assessing the concordance between the imputed genotypes and the observed genotypes at SNPs that are present on both the reference panel and the exome chip. Each of these SNPs was categorized according to the minor allele frequency (MAF) as rare (MAF ≤0.01), low-frequency (0.01< MAF <0.05) or common (MAF ≥0.05). Two measures of concordance were used: (i) overall concordance, defined as the proportion of imputed genotypes that were identical to the observed genotypes; and (ii) minor allele concordance, defined as the proportion of imputed genotypes with at least 1 minor allele that were identical to the observed genotypes, calculated only across the high-confidence low-frequency and rare SNPS (see Table S2). Of the 323 samples that remained after QC, 93 samples  were part of the population-level whole genome sequencing of Singapore Malays (SSMP) and Indians (SSIP) and these were excluded from the imputation analyses as they constituted part of the haplotype reference panels used for imputation.Quality checks of the genotype data for the 360 samples in iOmics yielded a final data freeze of 110 Chinese, 108 Malays and 105 Indians with high fidelity data for both Omni2.5 and the exome chip. The number of SNPs after QC ranged between 2,358,215 and 2,358,634 for Omni2.5, and between 272,680 and 272,857 for the exome array, of which there were at least 39,631 SNPs that were present on the exome array and the Omni2.5 , SSMP or SSIP, implying that these are the variants can never be recovered from imputation. However, on the basis of 110 Chinese, 108 Malays and 105 Indians that were genotyped on the exome array, almost all of these variants (>95%) are monomorphic in the three Singapore populations  1KGP; (ii) 1KGP and SSMP; (iii) 1KGP and SSIP. The SSMP and SSIP consisted of 96 Singapore Malays and 36 Singapore Indians respectively that had been whole-genome sequenced to a target coverage of 30X. Two definitions of imputation success were used.S2).A SNP was defined to be successfully imputed if the information metric generated as part of the imputation process was at least 0.3 (IMPUTE info ≥0.30), and if at least 95% of the samples presented a valid genotype call when the imputed genotype posterior probabilities were compared against a threshold of 0.90. The latter metric was akin to a SNP call rate of at least 95%. Jointly these two metrics indicated the extent of correlation between a target SNP with neighboring SNPs, and whether the imputation produced high-confidence calls. As expected, when imputed using the SNPs present on the Omni2.5, in excess of 75% of the common polymorphic SNPs on the exome chip were imputed successfully whereas less than 45% of the rare polymorphic SNPs were successfully imputed regardless of the choice of reference panels Figure 2While it appeared surprising that supplementing 1KGP with the Indian-specific panel did not produce more high quality imputation for the Indians, it was important to note the above two definitions of imputation quality fundamentally relied on the correlation structure between the target SNP and surrounding markers in the reference panel. The imputation algorithm can mistakenly bestow high-confidence calls when the underlying patterns of genetic correlation in the target sample differ considerably from the reference panel. The second measure of imputation success thus focused on the accuracy of the imputed genotypes, evaluated by the degree of concordance between the imputed genotypes and the observed genotypes at SNPs present on the Omni2.5 but absent on the exome chip.S17). As the primary intention for the use of the exome chip was to discover phenotype associations with rare and low-frequency SNPs, we additionally evaluated the minor allele discordance – defined as the discordance between imputed and observed genotypes that carried at least one minor allele at rare and low-frequency SNPs. This revealed that supplementing the 1KGP panel with population-specific haplotypes can significantly reduce the error rates of imputing the rare and low-frequency variants by up to 14.86% and 8.44% for the Malays and Indians respectively on the Omni2.5 (Tables S18). Table S19 shows the reduction in error rate with respect to Human1M as the study panel.While increasing the size of the 1KGP reference panel with at least 36 more samples improved imputation accuracy, the greatest improvements were seen when the respective population-specific panels were used to supplement the 1KGP to impute the Malays and Indians, where up to 0.55% decrease in discordance was observed for low-frequency SNPs in the Malays when using the Omni2.5 SNPs Table 1.Table S20). In contrast, 43% of the full set of exonic variants in SSMP (58.42% for SSIP) can be recovered successfully  by imputing off the 1KGP reference panel (Table S21). When a population-specific reference panel of at least 36 individuals was supplemented, the proportion that was successfully recovered increased to 47.20% for SSMP and 62.21% for SSIP .We have investigated the relevance of the Illumina HumanExome array for three populations in Southeast Asia comprising the Southern Han Chinese, Southeast Asian Malays and South Asian Indians. We observed that more than 80% of the content of the exome chip was monomorphic when assessed in about 100 individuals from each of these populations. Of the remaining SNPs that were polymorphic, at least 55% could be imputed successfully even with the use of early generation GWAS arrays such as the Illumina HumanHap550K or the Illumina Human1M, together with the haplotype reference panel from Phase 1 of the 1KGP. Imputation accuracy was increased by supplementing the 1KGP panel with population-specific whole-genome sequencing of at least 36 individuals. What was striking was the exome chip only provided an actual coverage of 9.57% of the polymorphic exonic variants that were present from whole-genome sequencing a separate collection of 96 Malays , and imputation against the 1KGP alone could already recover at least 30.93% of these variants successfully with high confidence in the Malay population (42.24% for Indian population) based upon the lowest density content genotyping array HumanHap550. Supplementing the 1KGP reference panel with population-specific content further increased the recoverable coverage to at least 32.44% for Malay population (42.74% for Indian population) using HumanHap550 as study panel, far surpassing the coverage provided by the exome chip  which were not part of the design and for which we have deep whole-genome sequencing data for. We did not investigate whether the conclusions were similarly relevant for European populations, which contributed the greatest resource during the design of the exome chip. However, we similarly noticed that in excess of 80% of the SNP content for the exome chip was monomorphic in the Chinese, despite the inclusion of samples of Chinese ancestry in the design. In addition, we will like to emphasize that our categorization of the SNPs into the four minor allele frequency bins of monomorphic, rare, low-frequency and common were made on the basis of our existing samples, and it is entirely possible that as the number of samples genotyped increases, a fraction of the SNPs currently categorized as monomorphic can actually turn out to be polymorphic albeit with low allele counts.From the perspective of cost, at the price of US$50 per exome chip (ignoring manpower and other infrastructure expenses) and US$3000 to perform 30-fold whole-genome sequencing of one genome, the cost to genotype 2,100 samples on the exome chip is equivalent to sequencing 35 samples. However, we have shown that the information generated from the whole-genome sequencing of at least 35 samples, together with publicly available information from the 1KGP, delivers a more comprehensive coverage of the exonic variants for the new population than additionally genotyping samples with the exome chip. The sequencing information can also be naturally extended to impute other populations as well, thereby benefitting multiple projects. Based on this assessment, we recommend that it is more cost-effective to perform deep whole-genome sequencing of a small subset of the samples in order to generate population-specific reference haplotype maps to complement the 1KGP, than to extend the use of the exome chip to studies with pre-existing GWAS data.Our assessment of imputation performance focused on different aspects of quality and accuracy. We particularly highlighted the difference between: (i) SNP-level imputation confidence, as measured by the information content and the peakedness of imputed genotype posterior probabilities ; and (ii) inherent accuracy between the imputed genotypes and the actually observed genotypes. While the latter metric is more appropriate in assessing imputation performance, it is often not possible to evaluate this agnostically as this requires the target set of SNPs to have already been genotyped. As such, the information criterion from imputation is often used as a surrogate measure of imputation quality. Jallow and colleagues illustrated that relying on the information criterion can produce erroneous outcomes, since imputation can mistakenly bestow high-confidence calls when the underlying haplotype structure in a target sample differs from that present in the reference panel In addition to measuring overall accuracy between imputed and observed genotypes, we specifically measured the discordance at observed genotypes that carried at least one minor allele at low-frequency and rare SNPs. There are two reasons here: (i) given the low frequency of the minor alleles at such SNPs, miscalling all the genotypes as major allele homozygotes only contributes a small degree of discordance and this can send the incorrect impression that imputation is highly accurate (i.e. >99% accuracy for a rare variant incorrectly imputed as monomorphic); (ii) the use of the exome chip is primarily intended to identify association with low-frequency and rare SNPs, and erroneously calling the presence or absence of a minor allele genotype can affect the power and false positive rate of the association analyses, especially when it is common to use statistical methods that pool allele counts across multiple SNPs in a gene region. Thus, in our evaluation of the use of imputation to recover exonic SNPs, we specifically assessed the ability to accurately determine the minor allele genotypes.One criticism to imputing the exonic variants is the need to address imputation uncertainty in during downstream analyses, in order to maximize statistical power When it comes to querying low-frequency or rare variants in the genome, there is likely to be no cost-effective replacement for sequencing. While genotyping with pre-designed microarrays has offered spectacularly success at representing common genomic content, our evaluation here has shown there is no significant advantage in additionally genotyping samples with GWAS information on the exome chip, over what statistical imputation with existing haplotype reference panels can already provide. If any, performing population-level deep whole-genome sequencing for as many subjects as possible for the cost of genotyping a GWAS cohort on the exome chip may yield greater returns.Figure S1The percentage of polymorphic exome chip SNPs in each of the three populations that can be reliably imputed against three different reference panels using the SNPs on the Illumina HumanHap550 as input. Each of these SNPs is categorized according to the minor allele frequency (MAF) as rare (0%< MAF ≤1%), low-frequency (1%< MAF ≤5%) and common (MAF >5%).(TIF)Click here for additional data file.Figure S2The percentage of polymorphic exome chip SNPs in each of the three populations that can be reliably imputed against three different reference panels using the SNPs on the Illumina Human1M as input. Each of these SNPs is categorized according to the minor allele frequency (MAF) as rare (0%< MAF ≤1%), low-frequency (1%< MAF ≤5%) and common (MAF >5%).(TIF)Click here for additional data file.Table S1Details on samples removed during the quality control process.(DOCX)Click here for additional data file.Table S2Number of SNPs remaining after the quality control process, assessed on the basis of the post-QC samples.(DOCX)Click here for additional data file.Table S3Number of SNPs available after rebuilding the Illumina HumanHap550 and Human1M from the Omni2.5.(DOCX)Click here for additional data file.Table S4Total number of imputed SNPs using 1000 Genome (1KG) Reference panel and Illumina HumanOmni2.5 as the study panel.(DOCX)Click here for additional data file.Table S5Total number of imputed SNPs using 1000 Genome (1KG) Reference panel and rebuilt Illumina HumanHap550 as the study panel.(DOCX)Click here for additional data file.Table S6Total number of imputed SNPs using 1000 Genome (1KG) Reference panel and rebuilt Illumina Human1M as the study panel.(DOCX)Click here for additional data file.Table S7Total number of imputed exome SNPs with info ≥0.3 that have call rate ≥95% in the Chinese, based on the SNPs on the Omni2.5.(DOCX)Click here for additional data file.Table S8Total number of imputed exome SNPs with info ≥0.3 that have call rate ≥95% in the Malays, based on the SNPs on the Omni2.5.(DOCX)Click here for additional data file.Table S9Total number of imputed exome SNPs with info ≥0.3 that have call rate ≥95% in the Indians, based on the SNPs on the Omni2.5.(DOCX)Click here for additional data file.Table S10Total number of imputed exome SNPs with info ≥0.3 that have call rate ≥95% in the Chinese, based on the SNPs on the HumanHap550.(DOCX)Click here for additional data file.Table S11Total number of imputed exome SNPs with info ≥0.3 that have call rate ≥95% in the Malays, based on the SNPs on the HumanHap550.(DOCX)Click here for additional data file.Table S12Total number of imputed exome SNPs with info ≥0.3 that have call rate ≥95% in the Indians, based on the SNPs on the HumanHap550.(DOCX)Click here for additional data file.Table S13Total number of imputed exome SNPs with info ≥0.3 that have call rate ≥95% in the Chinese, based on the SNPs on the Human1M.(DOCX)Click here for additional data file.Table S14Total number of imputed exome SNPs with info ≥0.3 that have call rate ≥95% in the Malays, based on the SNPs on the Human1M.(DOCX)Click here for additional data file.Table S15Total number of imputed exome SNPs with info ≥0.3 that have call rate ≥95% in the Indians, based on the SNPs on the Human1M.(DOCX)Click here for additional data file.Table S16Discordance (%) between imputed genotypes and actually observed genotypes at highly reliably imputed exome SNPs using HumanHap550 as the study panel.1 Phase 1 of the 1KGP, consisting of 1,092 subjects. 2 Singapore Sequencing Malay Project, consisting of 96 Southeast Asian Malays that have been whole-genome sequenced at 30X. 3 Singapore Sequencing Indian Project, consisting of 36 South Asian Indians that have been whole-genome sequenced at 30X.(DOCX)Click here for additional data file.Table S17Discordance (%) between imputed genotypes and actually observed genotypes at highly reliably imputed exome SNPs using Human1M as the study panel.1 Phase 1 of the 1KGP, consisting of 1,092 subjects. 2 Singapore Sequencing Malay Project, consisting of 96 Southeast Asian Malays that have been whole-genome sequenced at 30X. 3 Singapore Sequencing Indian Project, consisting of 36 South Asian Indians that have been whole-genome sequenced at 30X.(DOCX)Click here for additional data file.Table S181 at rare and low-frequency SNPs using HumanHap550 as the study panel.Discordance (%) between imputed genotypes and actually observed minor allele genotypes1 A minor allele genotype is defined as a genotype that carries at least one copy of the minor allele, and discordance here is measured against the total number of observed minor allele genotypes at rare and low-frequency SNPs. 2 Phase 1 of the 1KGP, consisting of 1,092 subjects. 3 Singapore Sequencing Malay Project, consisting of 96 Southeast Asian Malays that have been whole-genome sequenced at 30X. 4 Singapore Sequencing Indian Project, consisting of 36 South Asian Indians that have been whole-genome sequenced at 30X.(DOCX)Click here for additional data file.Table S191 at rare and low-frequency SNPs using Human1M as the study panel.Discordance (%) between imputed genotypes and actually observed minor allele genotypes1 A minor allele genotype is defined as a genotype that carries at least one copy of the minor allele, and discordance here is measured against the total number of observed minor allele genotypes at rare and low-frequency SNPs. 2 Phase 1 of the 1KGP, consisting of 1,092 subjects. 3 Singapore Sequencing Malay Project, consisting of 96 Southeast Asian Malays that have been whole-genome sequenced at 30X. 4 Singapore Sequencing Indian Project, consisting of 36 South Asian Indians that have been whole-genome sequenced at 30X.(DOCX)Click here for additional data file.Table S20Number of overlapping exonic exome variants with whole genome sequencing data.1All exonic exome: Malay  = 249,940; Indian  = 249,821. 2Polymorphic exonic exome: Malay  = 28,528; Indian  = 28,474. 3Proportion of polymorphic exonic exome is defined as the number of overlap polymorphic exonic exome divide by the total number of exonic SSMP/SSIP respectively. 4SSMP and SSIP variants are all polymorphic.(DOCX)Click here for additional data file.Table S21Actual and recoverable content of exonic variants in 96 Malays (SSMP) and 36 Indians (SSIP) based on HumanHap550 as the study panel.1Overlap HumanHap550 is the total number of exonic variants from SSMP or SSIP that overlaps with genotypes from HumanHap550 array. The number of imputed variants does not include these overlapped variants.(DOCX)Click here for additional data file.Table S22Actual and recoverable content of exonic variants in 96 Malays (SSMP) and 36 Indians (SSIP) based on Human1M as the study panel.1Overlap Human1M is the total number of exonic variants from SSMP or SSIP that overlaps with genotypes from Human1M array. The number of imputed variants does not include these overlapped variants.(DOCX)Click here for additional data file.


Like many other ancient genes, the cystic fibrosis transmembrane conductance regulator (CFTR) has survived for hundreds of millions of years. In this report, we consider whether such prodigious longevity of an individual gene – as opposed to an entire genome or species – should be considered surprising in the face of eons of relentless DNA replication errors, mutagenesis, and other causes of sequence polymorphism. The conventions that modern human SNP patterns result either from purifying selection or random  drift were not well supported, since extant models account rather poorly for the known plasticity and function (or the established SNP distributions) found in a multitude of genes such as CFTR. Instead, our analysis can be taken as a polemic indicating that SNPs in CFTR and many other mammalian genes may have been generated—and continue to accrue—in a fundamentally more organized manner than would otherwise have been expected. The resulting viewpoint contradicts earlier claims of ‘directional’ or ‘intelligent design-type’ SNP formation, and has important implications regarding the pace of DNA adaptation, the genesis of conserved non-coding DNA, and the extent to which eukaryotic SNP formation should be viewed as adaptive. The classically hypothesized, random accumulation of single nucleotide polymorphisms (SNPs) through the ages presents a paradox. As a variation on the ratchet mechanism sometimes attributed to Muller In semblance to much of the human genome, CFTR is largely non-coding , and like many other human genes has been preserved across diverse species including ancient fish, amphibian, fowl, and mammalian. A great deal is known regarding the genetics and physiology attributable to homozygous or heterozygous CFTR loss in humans. Complete functional absence of one copy of CFTR occurs in 3–4% of American and European Caucasians  Mutations in CFTR or any other eukaryotic gene continue to accrue until a threshold of deleterious SNPs is reached, beyond which the profound resilience and plasticity of individual proteins, as well as their crucial epistatic effects (due to multiple loci impacting protein function) will begin to falter. In this report, we argue that over hundreds of millions of years and ongoing SNP accrual, a threshold of this sort should have been expected for CFTR long ago. Note that when individuals or organisms with severe homozygous CFTR defects are culled by purifying selection, this would not overcome a steadily accumulating mutational burden present among surviving contemporaries and their descendants, each being subject to steadily advancing numbers of SNPs over the evolutionary time scale. While overall SNP diversity within a population may fluctuate due to factors such as selection or drift, ongoing accumulation of new DNA variants is very large, and by itself suggests a number of interesting considerations. Population genomics has modeled DNA persistence and stability based on recombination  or a cumulative loss of fitness (attributable to randomly accumulating SNPs and their gene interaction networks) together with natural selection to eliminate detrimental CFTR alleles. Neither of these mechanisms, however, would overcome the continued  accrual of SNPs among surviving members of a population. Our report furnishes recent genomic evidence that SNP accrual over vast numbers of generations could by now have left every CFTR allele so riddled with polymorphism that few, if any, would be viable (regardless of the extent of negative selection), and none would be available to recombine or restore a functional sequence. Moreover, removal of frequent unfit individuals  by natural selection would not reverse an accumulating CFTR mutational burden within surviving individuals and clades.Statistical and population-based approaches intended to explain a species averting “mutational meltdown” have not fully addressed emerging knowledge regarding haplosufficiency of vertebrate genes, deep plasticity of the vertebrate genome, the observation that protein coding sequences such as CFTR have been remarkably conserved over hundreds of millions of years (despite an assumption of ongoing SNP accumulation) and new evidence relevant to structure/activity of eukaryotic proteins, including their redundancy and/or expendability. While acknowledging that even a modest decrease in fitness has never been established for the vast majority of random SNPs in any higher eukaryotic gene, an inference has often been that recondite evolutionary pressure somehow holds back SNP accumulation in coding sequences such as CFTR. Because this is a testable hypothesis, we developed our study to address the following questions: 1) In a survey of human populations worldwide using modern and leading-edge genomic tools, including studies conducted to minimize ascertainment bias, what can fundamental patterns of SNP accumulation in CFTR and other critical genes tell us about the production of DNA variants (and particularly the random nature of SNPs at the time of their formation)?, 2) Do these patterns appear to be either spatially or temporally neutral with respect to natural selection?, and 3) Based on a current understanding of CFTR-dependent effects on fitness, what does the analysis indicate regarding the role of purifying selection over the course of human or more ancient hominid evolution? Our findings suggest that modern human SNP compendia are not well reconciled with traditional explanations for long-term DNA persistence (including the role of purifying selection), while at the same time providing no evidence for less conventional  within the exome; i.e. non-coding DNA can sustain small sequence variations with minimal adaptive consequence http://pilotbrowser.1000genomes.org/index.html) that were prospective, nonbiased, and manageable in terms of computing.Data from dbSNP and HapMap are not optimal for addressing SNP frequency or distribution, since ascertainment bias skews these compendia towards SNPs: 1) discovered previously from selective exonic or other sequencing programs (dbSNP and HapMap), or 2) desirable from the standpoint of hapblock structure; i.e. specifically being sought as ‘informative’ vis-à-vis genome wide or other surveys (HapMap). On the other hand, data from 1000 Genomes provides unbiased and valuable information in this regard. We utilized complete human genomic sequences (approx. 1.8 million SNPs) from an initial 1000 Genomes release (−46) was observed when critical human loci shown here were surveyed. A more expansive analysis of 4857 accessible genes indicated a similar ratio of 1∶1.8 (exonic∶intronic).Results in Is this difference in SNP frequency simply attributable to adaptive purging of deleterious exonic SNPs? If so, it becomes necessary to argue that approximately half of all single nucleotide changes across the human exome  were instead highly significant, and that a sizeable number of these (approximately 50%) have been expunged (e.g. due to premature death or decreased fitness). If one accepts the notion that non-coding DNA is also a frequent object of selective pressure , even greater numbers of exonic SNPs would need to be removed to account for the findings. In addition, as discussed in detail below, the observation is anything but ‘neutral’ or ‘random’ http://www.utoledo.edu/med/depts/bioinfo/database.html)) indicated a ratio of 1.3∶1. A similar conclusion has been reached by others in a study based on dbSNP Synonymous polymorphisms are often viewed as insignificant from the standpoint of protein function, and are typically disregarded in genome scale studies of human disease  www.genet.sickkids.on.ca). The transition-favoring aspect has been mechanistically ascribed to a failure of DNA error detection/repair, differences in misincorporation rates, or other factors Intronic SNP sequences from CFTR were examined and found to have a relative paucity of transversion  in comparison with transition type polymorphisms , Han Chinese (CHB), Japanese (JPT), and Yoruba  ethnic groups.SNP incidence profiles such as those shown in the CFTR minor allelic frequency (MAF) block diagram  obviously cannot be explained by recent mutation in human DNA followed by purifying selection: no MAF block would otherwise be present. Such findings are attributable to ancestral haplotypes—the major source of human polymorphism—with a presumption that large haplotype blocks degenerate over evolutionary time due to recombination no SNPs on the Y attributable to founder haplotype). Results from HapMap, dbSNP, or 1000 Genomes are shown in The Y-chromosome furnishes an independent test of SNP derivation by minimizing contributions of ancestral alleles . Anything less than 50% can be conditionally attributed to founder  derived autosomal alleles. Because the measured SNP incidence of the Y  As introduced above, in the absence of natural selection, the expected ratio of non-synonymous to synonymous polymorphisms on a full genome basis is usually taken to reflect stochastic SNP formation, since factors such as drift, shift, etc. would be minimized by random assortment. A neutral or random accrual of exonic SNPs generates a quantitative ratio of approximately 3∶1 . The “expected” incidence across 90,000 exonic SNPs  is therefore at least a reversal of the “observed”—i.e. ∼68,000 non-synonymous and ∼22,000 synonymous SNPs. (Codon usage as a means to preserve exonic DNA is considered later in this report.)First, the mutation rate in humans  ratio of 1∶3 (synonymous: non-synonymous) to the observed ratio of ∼1.5∶1. This would indicate that more than three of every four (approx. 78%) non-synonymous SNPs in CFTR and other genes have been deleted (or at least markedly de-enriched) from the gene pool either by classic (purifying) selection, or due to more complex, multigenic effects. The model presents a “best case” scenario, since rare mutations with a positive effect on fitness would require still higher rates of purifying selection to arrive at the same net loss of non-synonymous polymorphism. Note this analysis is quantitative and does not involve statistical or other underlying demographic assumption.  genomic enrichment for synonymous vs. non-synonymous human SNPs, the expected ratio of synonymous: non-synonymous SNPs if formation was random—a value obtained from the genetic code, the best available (and readily quantified) degree of human DNA polymorphism, and size of the human genome.) The available and published findings point to a decrease from ∼68,000 (predicted) to ∼36,000 (observed) non-synonymous SNPs, or roughly 32,000 pre-reproductive age deaths or unfit genomes deleted or severely repressed during a finite period of human  evolution.The assertion that an estimated 32,000 single nucleotide changes  have been expunged from the gene pool of two individual humans with a common (pre-human) ancestor highlights the rarity of these mutational events. The notion that an isolated, randomly placed non-synonymous SNP in a diploid gene such as CFTR should not only abrogate function, but also lead to death or blunt fertility of the entire organism seems incompatible with the puissant effects on fitness that would be required to explain the modern SNP distributions reviewed here. Put plainly, it seems antithetical to suppose that a single, random base substitution in any gene should disrupt activity or severely undermine fitness of an entire human, let alone that this has occurred tens of thousands of separate times over the course of an individual's evolutionary descent from an ancestral founder.any human gene would usually  cause death or severely abrogate fitness is at odds with much of what has been learned about human gene and protein plasticity over the past 50 years.As shown in Homo sapiens is a comparatively young species. If this level of polymorphism represents a general threshold (i.e. a “tipping point”) beyond which fitness is lost as genomes decompensate, it is difficult to imagine how a panoply of much more ancient genes (among far more ancient species) could have survived during an evolutionary period thousands of times more prolonged. In addition, note that in recombinant murine models, an extensive database already exists with regard to the same question. As with human, the likelihood that a single (random) non-synonymous SNP per murine gene should be a common cause of infertility or death is diminishingly small based on a vast number of transgenic animals and experimental findings. Yet the data reviewed in Imagine that technology were available to place one exonic SNP in a single diploid gene randomly in the human genome. The likelihood that this individual SNP would be sufficient to destroy (or render less fertile) an entire individual is remote. Moreover, 5 mutations would be expected to distinguish a present-day individual from an early hominid ancestor. Now apply this same process for a much longer period (among significantly older metazoans) and, for the moment, omit the role of purifying selective pressure. After 250×106 years (the evolutionary age of many sharks), mutations would be expected in at least every codon of any sizable vertebrate genome; e.g., every codon in a 3×109 base pair genome would be altered in a random fashion. If a more realistic generation time (e.g. one year) is imposed, every codon in every core metabolic gene would be altered 20 times, and in a genome of 30,000,000 bp (perhaps more representative of certain diploid ancestors), each codon in every core metabolic gene would be randomly replaced nearly 2,000 times. Modestly lower rates of human mutation Our earliest hominid ancestors inherited CFTR as part of a genetic legacy hundreds of millions of years old. Irrespective of whether or not modern CFTR SNP patterns are attributable to recent purifying selection, the integrity and flexibility of the human gene is well-established. How did CFTR persist without becoming riddled with polymorphism despite its ancient origins and subsequent epochs of mutation accrual? With regard to a central question posed by this report, consider hominid evolution during the past 200,000 years and a DNA mutation rate (described above) with approx. 90 new SNPs per individual per generation . When complete coding sequences from 629 individuals and a recent 1000 Genomes release were analyzed, approx. 69% of positions found to be polymorphic among both humans and multiple other species were synonymous (p = 3.2×10−43 versus the expected number of synonymous SNPs if accumulation were randomly distributed). Since variability is enriched in a strongly synonymous fashion, these mutations are unlikely to represent DNA positions where SNPs have been established by purifying selection. Moreover, since a robust synonymous bias occurs genome wide, neutrality or drift do not furnish a satisfactory explanation. In a related study, we observed that CFTR exonic concordance among four vertebrate species evolutionarily distant from human  is approximately 43% and that the total number of single nucleotide differences between these four species and human CFTR is 4620. The preponderance of shared SNPs among the four species was again found to be synonymous. In addition, when a SNP was applied at random in 4620 distinct instances to the 4443 base pair open reading frame of human CFTR using computer simulation, concordance between human and non-human coding sequences was much lower than observed in nature   . ObservaRecent studies of CFTR sequence evolution Note that the above statement is by no means meant to imply that evolutionary selection does not purge deleterious mutation. However, insofar as human DNA is concerned, even if one adopts a very conservative estimate that a single SNP anywhere in the ∼90 million nucleotides of human coding DNA has a 1 in 10 chance of causing death or undermining fitness of the entire human organism, we are left with an estimate that among SNPs expected on a random basis, 45% of all exonic SNPs and>70% of non-synonymous SNPs instead were never produced. Below we provide a summary that underscores the topics dealt with by this report. and Table S1 establish that introns exhibit a strong increase in SNP frequency whether investigated among a selected gene set or across the entire human genome . The assumption that this results from selective removal of exonic SNPs seems unsatisfactory, since it would require at least one of every two coding SNPs (including a preponderance of synonymous SNPs) to be markedly detrimental, leading to early death or otherwise undermining fertility, irrespective of epistasis . The likelihood that a solitary, randomly placed exonic SNP in CFTR (or other gene) should be lethal or vitiate fertility is contrasted by a substantial body of modern evidence regarding protein function and plasticity. We believe an alternative explanation has not been adequately considered; namely, that the modern SNP distributions shown here are attributable in large measure to a strong bias in their original formation. , heterozygous positions attributable to very recent SNP formation among congenic murine lines removed from many forms of selection , we measured a ratio of 1.6∶1 synonymous to non-synonymous substitutions (p = 8.3×10−49) (specific mechanisms that could account for pathways of this type are described later in this report.The data in formation has been much more frequent (on a per nucleotide basis) in non-coding (compared to coding) DNA, and that exonic SNPs are far more likely to be created as synonymous (versus non-synonymous) variants.Note that few (if any) studies have considered the possibility that modern human SNP patterns might depend more on the ways mutations were originally produced than the extent to which they have subsequently been selected or randomly fixed. SNP formation bias is typically neglected by current models of both exonic and protein evolution. Yet our data suggest that hominid SNP Finally, data in other Figures and Tables of the report (and throughout the text) offer a human genomic context for the well-described mutational “ratchet” scenario contemplated years ago by Muller and colleagues. One can argue that over eons of eukaryotic evolution, a mutational burden should (by now) have decimated CFTR and other diploid genes. Yet despite hundreds of millions of years of ongoing mutation accrual in core metabolic proteins, the human genome does not appear to be anywhere near “meltdown.” The notion that spectacular longevity of higher eukaryotic DNA is somehow accounted for by selective elimination of detrimental SNPs does not adequately explain the findings. Although it is clear that individuals (and entire species) are constantly being expunged from the global gene pool, this would have no effect (and would not reverse) ongoing SNP accumulation in the genes or genomes of all surviving individuals (and species) whose burden of DNA mutation is not resolved simply by “weeding out” of others. SNP accrual over countless generations might be expected to leave every allele riddled with polymorphism (regardless of negative selection), and eliminate sequences that could otherwise recombine to restore a functional protein. This dilemma has not been fully considered in light of modern functional genomics; computer simulation indicates “mutational meltdown” should have occurred millions of years ago.Perhaps SNP generation over the evolutionary timescale has been fundamentally less random than we typically assume. Under this arrangement, the major (high MAF) human SNPs in HapMap and 1000 Genomes would have appeared in a fashion configured to prevent overwhelming genomic attrition . Such a model would help reconcile observations that human SNP populations (both synonymous and non-synonymous) are non-random  in distribution , yet aremechanism that opposes genetic dissipation and dictates patterns of SNP accrual that are allowable, since random replacements by transition nucleotides (as opposed to transversions) at the 3rd codon position overwhelmingly (by 94%) favor synonymous substitution. Moreover, a transition at any codon position confers a bias towards both synonymous and conservative amino acid replacement  et al., manuscript submitted). Because the same SNP promoting elements were otherwise conserved across murine exons, introns, and intergenic regions, this result could not manageably be attributed to ongoing purifying selection at the level of protein function. Instead, the data suggested an exon-sparing mechanism that regulates SNPs at the time of their formation and serves to minimize mutations within exonic DNA. The pathway was best interpreted as another device that co-evolved with both the genetic code and codon usage to help preserve the exome of higher organisms.In unpublished studies, we recently compiled a genomic analysis of sixteen distinct strains of In contrast to exonic DNA, significant numbers of intronic and intergenic SNPs are already known to be generated in a constrained and arguably predictable manner based on transition bias, proximity to DNA recombination sites, nucleosome structure, GC rich isochores, or specific sequence contexts Drosophila, human, and other species, including seminal observations from Kreitman and Colleagues DrosophilaThe results presented in this report should be viewed in light of earlier, population studies regarding natural selection and the consequent distribution of SNP fitness effects. For example, our finding of synonymous SNP enrichment agrees with previous data in In this study, we also considered more classical, population-based analysis of natural selection utilizing McDonald-Kreitman methodology to test human CFTR variants from over 1,000 individuals and numerous ethnicities (1000 Genomes), and compared this data to an outgroup representing the most recent chimpanzee sequence (CHIMP 2.1.4) The proportion of base substitutions fixed in human versus differences between human and chimp indicated α = (−) 2.72, with neutrality index of 3.72 . Based on classical interpretation, our findings could be taken to suggest a trend towards positive CFTR selection with non-neutral divergence for the human CF gene. On the other hand, evidence presented here indicates synonymous SNP formation is often non-neutral due to features such as transition bias. This aspect complicates a conventional assessment of selection intensity, which (as above) typically requires a well-defined cohort of random and fitness neutral (e.g. synonymous) SNPs for comparison to non-synonymous variants. For example, we show that CFTR (like many other genes) exhibits discrete exonic regions with high CpG content –5. Such In this report, we interpret proteins and their constituent exons as ‘lessons’ of inestimable value picked up from iterative attempts at long term DNA survival. We maintain that despite the need for variation, lessons such as these should be viewed as far too valuable to expend—particularly when alternatives such as non-coding DNA might be utilized instead. We suggest that SNP formation in CFTR and other human genes appears configured to help preserve exons , constrained by specific molecular mechanisms such as those involving transition bias and codon usage, and should therefore be viewed as meaningful and adaptive. A precedent for the sort of adaptation proposed here has already been described for a different evolutionary mechanism, the combinatorial immune system, where antediluvian trial and error turned up a highly organized means of generating extensive diversity that is capable of responding to infectious agents not yet encountered by a species. We note that the adaptive immune response provides remarkable phenotypic variation that is regulated, has worked well through the ages, is readily explained without evoking directional evolution In summary, more attention should to be paid to ways in which production of human DNA polymorphism (i.e. at the time of SNP formation) is organized. A review of quotidian sequence data provided throughout this report indicates that genome-wide SNP patterns should be evaluated in light of long-term DNA survival and modern knowledge regarding protein plasticity. If HapMap, 1000 Genomes, and related projects in other species increasingly reveal predictable or mechanistically relevant patterns of SNP distribution as contemplated by the present study, evolution might be viewed as more regulated than has been classically interpreted. This report therefore suggests the existence of mechanisms by which DNA may regulate its own diversity, safeguard the hard-earned lessons encoded by genes, and help guide its own evolutionary path.http://www.utoledo.edu/med/depts/bioinfo/database.html), and a 1000 Genomes release  were evaluated. The length of each coding sequence and the combined lengths of the intronic sequences (Exon-Intron Database), together with SNP information (1000 Genomes) were obtained. SNP totals were normalized to numbers of exonic or intronic nucleotides .SNPs in 133 human genes known to be lethal or severely debilitating when deleted http://www.utoledo.edu/med/depts/bioinfo/database.html and 1000 Genomes). Synonymous and non-synonymous SNPs were normalized to total exonic nucleotide content as above.For evaluation of synonymous versus non-synonymous mutations, two datasets were used: SNPs in 98 of the 133 genes described above with at least one exonic variant, and 13,820 genes for which data was accessible  software. We tested these for features shown previously to indicate sequencing error, and found less than 2–3% of SNPs exhibited low quality score, inconsistent consensus, misalignment, artifactually high “coverage” (“pile-up”) due to homologous sequences elsewhere in the genome, duplicated reads, indels, short local repeats, etc. This result provided independent confirmation for robustness of SNP data available from the 1000 Genomes resource.http://hapmap.ncbi.nlm.nih.gov/). Minor allelic frequency (MAF) refers to the relative frequency (1000 = 100% incidence) of the minor allele at each SNP position. We focused our attention on SNPs verified by HapMap and with minor allelic frequency greater than zero in at least one population . This convention allowed us to use HapMap as a less biased tool for cataloging genomic variation among very diverse individuals. In order to generate a MAF block diagram, allele frequency data was downloaded from the UCSC genome table browser (http://genome.ucsc.edu/cgi-bin/hg:tables). After downloading, SNPs with MAF equal to zero among all four ethnicities were omitted to enhance stringency. The remaining SNPs were then utilized to generate scatter plots.Frequency data for SNPs in CFTR or NF1 were collated for each of four ethnicities: JPT ; CHB ; CEU ; and YRI  using HapMap (http://www.genet.sickkids.on.ca/cftr/app). SNP totals for the Y and other chromosomes were obtained directly from dbSNP, HapMap, and 1000 Genomes, and presented in tabular form.SNP configurations (transition and transversion) for intronic and exonic regions using the set of 98 human genes described above with at least one exonic SNP were collated. Frequencies of CFTR SNP subtypes (transition vs. transversion) among>1000 SNPs, many of which have been implicated in clinical CF, were also tallied (SNPs were placed at computer-generated positions in 1) the full-length CFTR sequence, 2) a GC-rich region of CFTR  of the open reading frame), or 3) an artificial, assembled gene containing 1480 codons arranged randomly (i.e. random codons to generate a 4440 bp sequence).The site for each base substitution was determined by a random number function that served as an index into an array representing the desired base substitution ratio. For example, an unbiased base substitution array for the replacement of adenine contained cytosine, guanine and thymine in equal proportions, so that these were chosen with equal probability to replace adenine. The base substitution ratios were:Unbiased – each of the four bases was replaced with equal probability by any of the other three.http://www.genet.sickkids.on.ca/cftr/app) describe directional substitution, such as A to C, and were used to calculate base substitution frequencies.CFTR Mutation Database – All entries in this database , and since 1) the directionality is not relevant to this particular test of transition bias, and 2) there is clearly a transition bias regardless of directionality judged by studies of more recent mutations in CFTR, the incidences of both directions were analyzed together.Each simulation run imposed 10–50 base replacements on the human CFTR coding sequence or the artificial sequence, and 10 runs were conducted for each condition. In all instances, a site (base) was allowed to mutate more than one time, although in practice this seldom occurred. Due to the comparatively short length of the GC-rich region, 10 mutated sites were tested per simulation. Results from simulations were determined following translation and alignment with the authentic sequence.A modified version of the above algorithm was established to model the long term consequence of mutation accrual in CFTR. Similarity to an original CFTR sequence was evaluated by translation of the resulting cDNA after several thousand generations in order to evaluate protein integrity.Code for the above simulations was designed as follows:A sequence related to CFTR as described above was represented as a character array .Base replacement arrays were populated to reflect the particular substitution bias being simulated. For example, The particular position to be mutated was selected by a random number within the range of the length of the sequence to be mutated. This random number was used as an index into the array from Step 1.The sequence to be mutated was read as an array using the random number from Step 3. The base at that position was determined.For example, if position 5 of the sequence to be mutated contained an A, the A-substitution array (Step 2) would be consulted to determine the replacement base. A random number determined which of the three bases would replace the A. The random number was used to index the A-substitution array created and populated in Step 2.The position in the sequence to be mutated randomly selected in Step 3 was overwritten with the replacement base from Step 5.When the desired number of simulated mutations had been completed, the mutated sequence codons were compared with corresponding original sequence to determine the numbers of synonymous or non-synonymous mutations and (for the non-synonymous mutations) the numbers of conservative versus non-conservative amino acid replacements.http://www.ebi.ac.uk/Tools/clustalw2/index.html). The number of bases identical among the non-human species was divided by the largest total number of bases in any species to obtain the most conservative measure of percent concordance. Locations of exonic human SNPs in each gene were obtained from 1000 Genomes. Following alignment of all six non-human species, the variance among non-human genes was examined using human SNPs as a marker. For example, after observing a SNP in human CFTR at nucleotide position 593 and the corresponding position in six other species, a determination was made as to whether or not the non-human species also exhibited polymorphism at that site.CFTR and 21 other genes with ≥50% concordance across six non-human species  were obtained from the UCSC genome browser. Sequences of the non-human species were aligned using the ClustalW2 tool  were aligned with the human coding strand, both independently and collectively. In the collective alignment, ∼43% of the coding sequence was invariant. A computer simulation was conducted and the total number of differences from human placed randomly within the human CFTR reading frame of 4443 bp. This simulation was performed 120,000 times, and the total number of differences from human (distributed as random point mutations) was tabulated. The goal was to determine whether concordance observed in a multiple species alignment could be accounted for by chance.2 and contingency table analysis (2×2 tables employing Yates' correction for continuity).Comparison for SNP frequencies  were conducted by χFigure S1Minor allelic frequencies of prominent CFTR SNPs. The heat map emphasizes findings shown in (TIF)Click here for additional data file.Table S1SNP incidence in human intronic and exonic DNA. SNPs in 133 human genes known to be lethal or severely debilitating if deleted (TIF)Click here for additional data file.Table S2Synonymous and non-synonymous SNP incidence. Exonic SNPs in 98 of the 133 genes shown in (TIF)Click here for additional data file.Table S3Relationship between human SNPs and variable nucleotide positions of non-human species. From among 98 genes described in (TIF)Click here for additional data file.