Screening tests for Trisomy 21 (T21), also known as Down syndrome, are routinely performed for the majority of pregnant women. However, current tests rely on either evaluating non-specific markers, which lead to false negative and false positive results, or on invasive tests, which while highly accurate, are expensive and carry a risk of fetal loss. We outline a novel, rapid, highly sensitive, and targeted approach to non-invasively detect fetal T21 using maternal plasma DNA.Highly heterozygous tandem Single Nucleotide Polymorphism (SNP) sequences on chromosome 21 were analyzed using High-Fidelity PCR and Cycling Temperature Capillary Electrophoresis (CTCE). This approach was used to blindly analyze plasma DNA obtained from peripheral blood from 40 high risk pregnant women, in adherence to a Medical College of Wisconsin Institutional Review Board approved protocol. Tandem SNP sequences were informative when the mother was heterozygous and a third paternal haplotype was present, permitting a quantitative comparison between the maternally inherited haplotype and the paternally inherited haplotype to infer fetal chromosomal dosage by calculating a Haplotype Ratio (HR). 27 subjects were assessable; 13 subjects were not informative due to either low DNA yield or were not informative at the tandem SNP sequences examined. All results were confirmed by a procedure (amniocentesis/CVS) or at postnatal follow-up. Twenty subjects were identified as carrying a disomy 21 fetus (with two copies of chromosome 21) and seven subjects were identified as carrying a T21 fetus. The sensitivity and the specificity of the assay was 100% when HR values lying between 3/5 and 5/3 were used as a threshold for normal subjects.In summary, a targeted approach, based on calculation of Haplotype Ratios from tandem SNP sequences combined with a sensitive and quantitative DNA measurement technology can be used to accurately detect fetal T21 in maternal plasma when sufficient fetal DNA is present in maternal plasma. Fetal chromosomal abnormalities often lead to post-birth medical conditions requiring specialized medical care that result in emotional and financial challenges to the child, parent(s) and society. Important related medical conditions can be successfully managed during the neonatal period if diagnosed early during the course of pregnancy; provided that early and accurate diagnosis exists. Conversely, these challenges increase dramatically if diagnosis is delayed Several methods to develop a more accurate non-invasive prenatal test for T21 have been investigated. Analysis of fetal cells in maternal circulation has been difficult due to their fragility and scarcity Herein, we describe a novel, accurate, internally controlled, DNA-based approach to non-invasively detect fetal chromosomal abnormalities. We analyzed highly heterozygous, neighboring or “tandem” Single Nucleotide Polymorphism (SNP) sequences as short haplotypes Figure 1Text S1.Detailed methods are described in Table S2 online). The ethnicity of the subjects is listed also under S2. Buccal swabs from the father were also available for seven subjects. The paternal subjects were used to confirm the third peak in the maternal plasma was a paternally inherited allele.The study was approved by the Institutional Review Board (IRB) #1 for Human Research at the Medical College of Wisconsin (MCW study numbers PRO00005322 and PRO00009733). 40 high risk pregnant patients who were scheduled to undergo amniocentesis or CVS or had already had either of these procedures were recruited as subjects at Froedtert Hospital in adherence to the aforementioned IRB approved protocol. Written informed consent was obtained from each participant before blood draws and buccal swabs were obtained. Seven subjects carrying a fetus with T21 and 20 subjects carrying a fetus with normal chromosome 21 were recruited, including two subjects carrying a fetus with Trisomy 18. Karyotype analysis demonstrated that seven pregnancies carried a T21 fetus while two pregnancies carried a Trisomy18 fetus and 18 pregnancies carried a normal euploid fetus. The gestational week and maternal age varied from 9–36.1 weeks and 22–43 years respectively , 0.125 mM dNTPs , 0.04 Units/µl HotStar Taq DNA polymerase , and GC-rich primers at a final concentration of 0.6 µM, or 1× Buffer , 0.1 mM dNTPs , 100 ng/µl BSA , 2 µl of Pfu Ultra II Fusion HS DNA polymerase , 0.2 µM GC-rich primers , and nuclease free water in a final reaction volume of 50 µl. The linear amplification product was used as a template in setting up the “sequence-specific” PCR as a second step. Sequence-specific PCR was set up as a high-fidelity reaction using 3 ul of linear amplification product as template (see section High-Fidelity PCR and CTCE conditions for details).Multiplexed Linear Amplification (MLA) was performed on 18 subjects. Each sample was multiplexed with a panel of 10–15 potentially informative tandem SNP assays. Linear amplification reactions were set up using approximately 4 ng of genomic DNA, and final concentrations of 1× Buffer with 15 mM MgClEach PCR was set up using final concentrations of 1× Pfu Ultra II Fusion HS DNA polymerase buffer , 0.1 mM dNTPs , 2 µl of the Pfu Ultra II Fusion HS DNA polymerase , 100 ng/µl BSA , 0.2 µM sequence specific , genomic DNA and nuclease free water in a final reaction volume of 50 µl and run in a Mastercycler ep gradientS  thermal cycler under the conditions of 95°C for 2 min followed by 42 cycles at 95°C for 30 sec, annealing at 55°C for 30 sec, extension at 72°C for 30 sec and then the final extension at 72°C for 10 min. Approximately 25 ng of genomic DNA was used for testing the maternal buccal swab. When performing direct sequence-specific PCR, approximately 3 ng of genomic DNA was used when testing maternal plasma. When linear amplification was performed prior to the sequence-specific PCR step, approximately 4 ng of genomic DNA extracted from maternal plasma was used.Figure S1 online).All CTCE data was generated using a MegaBACE™, a high-throughput DNA analysis system  CTCE .txt data files were parsed and header and electropherogram data collected for each well in the plate. The peaks from amplified DNA corresponding to the allele of interest and control amplifications were detected and integrated using standard algorithms ). In order to maintain quality control, parameters such as relative signal to noise ratio for the electropherogram, overall intensity of allele specific and control peaks, and inter-peak distance were calculated. Analysis was performed using the Acknowledge 3.9.1  sliding window (delta time) to define peak areas and calculate Haplotype Ratios on an Excel spreadsheet .HR 95% confidence intervals were calculated for each subject as the mean ±1.96 Standard Deviation/√3. A Receiver Operating Characteristic (ROC) curve approach was applied to the mean of the triplicate assay to ascertain what cut point would give the highest sensitivity and specificity since T21 would be called if the values were close to 0, 0.5, or 2. For the ROC curve the inverse of values less than 1 was used. However in reporting the HR results we give the range of values for which a normal and T21 result respectively would be called.Figure S1 online). Tandem SNP PCR reactions and CTCE assays were optimized for the remaining 59 tandem SNP target sequences  was data-mined  and The maternal buccal swab or whole blood sample was first screened to identify heterozygosity at tandem SNP sequences; maternal plasma samples were processed for those that that were heterozygous. Tandem SNP sequences were considered informative when two peaks were observed in DNA extracted from maternal buccal swabs or maternal white blood cells, and three peaks were observed in DNA from maternal plasma, by CTCE analysis Figure 3Text S1 online). A total of 27 subjects were assessable, 18 subjects were pregnant women carrying a normal fetus, two were pregnant women carrying a Trisomy 18 (T18) fetus and seven were pregnant women carrying a T21 fetus. The two subjects carrying a T18 fetus served as additional controls because all of the tandem SNP sequences used in this study were targeted to chromosome 21. Haplotype ratios were determined and chromosome 21 calls were made for the 27 patient plasma samples based on the HR values as either T21 or Disomy 21 (D21). The HR values for 27 study samples are listed under Table S4 online. The 95% CI for T21 showed no overlap with the D21 and vice versa. Further, 100% sensitivity and 100% specificity was obtained for the mean triplicate values when HR values lying between 3/5 and 5/3 were called as D21 and any values outside this range were called as T21. Examples of HR analysis resulting in the detection of a normal or D21 fetus and a T21 fetus are shown in Figure S2 online. Ten tandem SNP sequences were informative on 27 study subjects (see Table S5 online). All 27 samples were correctly called and confirmed by either amniocentesis, CVS, or by postnatal karyotype.Forty high risk pregnant women were enrolled as subjects at Froedtert Hospital in strict adherence to MCW IRB protocols. One sample was lost during extraction (FDT0823), six samples did not have enough DNA to proceed with analysis due to low recovery (<2 ng), these were . Six samples were not informative at the 9–16 tandem SNP assays that were tested for informativeness before running out of sample   using NCBI build 36 of HapMap data (Phase II+III). Based upon this analysis, one tandem SNP target (rs432114–rs365433) which showed a third  peak on subject FDT0604 was not predicted to be highly informative. It was later determined that a third SNP resides between the two original SNPs of interest although it is unknown if this was the third, informative haplotype. Detection of novel variants is a previously recognized limitation of CTCE when being utilized as a genotyping method A major challenge of fetal DNA analysis using maternal plasma is the limited amounts of fetal DNA available per maternal blood sample . This in turn limits the number of possible assays that can be analyzed. Various methods for global amplification of DNA target molecules via whole genome amplification (WGA) are frequently utilized Text S1 .To amplify our tandem SNP target sequences, we optimized a multiplexed, linear amplification step. 5′ flap sequences  attached to the 5′ end of a primer is highly efficient during linear PCR . Indeed, frequently yields greater than 100% are observed, suggesting multiple primer initiations per parent DNA sequence. We hypothesized that primers with a GC-rich tail could be multiplexed and linearly amplified to avoid primer interactions without introducing allelic bias during amplification (see REN exon 1) were quantified by competitive PCR where a known amount of an artificial mutant PCR product containing a single base pair difference from the wild-type was added in to a REN gene sequence-specific master mix along with 1 ul of the sample “before linear amplification” or 1 ul of the sample “after linear amplification” to serve as an internal standard (see Text S1 online) Table S6 online).The yield (efficiency) of linear amplification was determined by assessing the copy number of the target sequences prior to and following linear amplification. Copies of the human renin gene (We then compared MLA products with samples that had been amplified by ligation-mediated PCR (LM-PCR), a commonly used technique in WGA This study supports the potential for tandem SNP analysis to be used as an effective and accurate means for non-invasive diagnosis of T21. Tandem SNP sequences are short , which is preferred in a targeted sequence-specific approach because fetal DNA fragments in maternal circulation are short. These short haplotypes are informative when a third haplotype is present in maternal plasma. The algorithm for analysis is straightforward. A HR is determined, which approximately compares the maternally inherited contribution to the fetus, |p1−p2|, to the paternally inherited contribution to the fetus, p3.a priori hypothesis of discrete HR outputs permits the establishment of population-based confidence intervals (standards) around these values, a significant advantage over approaches that test if fetal DNA concentrations are different between a chromosome of interest and a reference chromosome or genomic region. Because fetal DNA concentrations are highly variable and influenced by many factors , approaches which test for differences between fetal DNA concentrations at a chromosome of interest (i.e. chromosome 21) and a reference chromosome/genomic region must be performed at an individual level for each patient, and is statistically more challenging. In this study, the ROC curve would have allowed a cut point anywhere between 1.67 and 1.96 without changing sensitivity or specificity but additional studies will be required to establish the true threshold.The tandem SNP approach has several distinct advantages over other approaches which also analyze circulating fetal nucleic acids in maternal blood. First, HR analysis tests for discrete outputs; values of either 1 , 0 (trisomy), 0.5 (trisomy or deletion), or 2 (trisomy) are the only expected HR output. The Second, because an internal comparator exists for every informative tandem SNP sequence, the experimental variability introduced through the tandem SNP approach is smaller than other approaches requiring an external reference. This can be seen by first looking at the difference on the HR numerator . The difference of the two maternal haplotypes is the sum of the variances minus twice the covariance (positive since SNPs within this chromosome are positively correlated) between the two haplotypes. This is quite different when an independent external comparator is used. For example, a previous study employed a single SNP approach to calculate fetal DNA fractions on chromosome 21 Third, because the tandem SNP approach is targeted to potentially informative tandem SNP sequences, a limited number of regions are required for analysis. This simplifies the process, limits costs, and enables extension to large batch processing with quick turnaround. Methods employing a whole genome shotgun approach are costly both in dollars and time . It should be noted that previous publications suggest minor alleles present at 1% are easily and reproducibly detected by CTCE Fourth, unlike Short Tandem Repeats (STRs), which would also be potentially highly informative, tandem SNP sequences can be amplified with high fidelity and can also be multiplexed to a high degree.Finally, the tandem SNP approach has wide applicability for additional chromosomal defects, i.e., it could be used for the prenatal detection of trisomy 18, trisomy 13, and it could be extended to other regions on the genome and chromosomal abnormalities. Furthermore, the tandem SNP approach is not platform dependent. The general strategy of targeted analysis of tandem SNP sequences can be performed on multiple platforms, including technologies with high throughput such as next-generation sequencing.Figure S1DNA melting map of CTCE target sequence covering tandem SNPs (rs961301–rs2830208). Winmelt software  was used to identify the melting profile of the DNA sequences which calculates the thermal stability and denaturation behaviour of double-stranded DNAs and RNAs up to a length of 1,000 base pairs. The Winmelt algorithm is based on recursive generation of conditional and a priori probabilities for base stacking. The melting profile output of the program can be compared directly to the experimental results and thus this program is used to optimize the experimental conditions prior to CTCE analysis. All three haplotypes can be theoretically separated according to DNA melting temperature. Black line indicates haplotype 1 . Yellow line indicates haplotype 2 . Red line indicates haplotype 3 . This graph shows that the melting temperature for this target sequence is approximately 66°C.(0.21 MB TIF)Click here for additional data file.Figure S2Electropherogram for 25 study samples. Screenshots of Acknowledge software output for electropherograms of 25 maternal plasma samples . Each subject's result/plot is a combination of two panels where the top panel represents the buccal swab and the bottom panel represents maternal plasma. Along the x-axis is migration time (seconds/minutes) and the y-axis is Relative Fluorescence (volts). Peak inside the circle in bottom panel is the fetal peak . Peak areas for maternal plasma are measured to calculate HR values  online.(3.89 MB TIF)Click here for additional data file.Figure S3Reconstruction Experiments. Tandem SNPs rs2832141–rs2246777 were analyzed by CTCE in an infant-maternal pair. The top panel (A) is the electropherogram of only maternal DNA isolated from white blood cells (WBC) followed by (B) the electropherogram of DNA isolated from cord blood of a confirmed T21 infant, and (C) and (D) are electropherograms depicting the infant-maternal mixtures at 10% and 5%, respectively.(1.54 MB TIF)Click here for additional data file.Figure S4Box Plot for comparing Direct PCR to MLA in Reconstruction Experiments. The HRs are approximately 1 for the euploid reconstruction mixtures and close to 0 for the T21 reconstruction mixtures, irrespective of the protocol (Direct PCR or MLA) used. This demonstrates that HRs calculated following MLA are not significantly different from HRs obtained following Direct PCR. The simulated concentrations were aimed at 2.5%, 5%, 10%, 20%, and 40% for both a confirmed T21 infant - maternal mixture and a euploid infant - maternal mixture. True paternal percentages were approximately in the range of 2.4–4% for 2.5% mixture, 3.7–7% for the 5% mixture, 3–9% for the 10% mixture, 6–14% for the 20% mixture and 17–24% for the 40% mixture as estimated by CTCE.(0.13 MB TIF)Click here for additional data file.Table S1Maternal subjects assessed for Haplotype Ratios.(0.05 MB DOC)Click here for additional data file.Table S2Non-informative subjects.(0.04 MB DOC)Click here for additional data file.Table S3Primer sequences for 58 tandem SNP pairs.(0.08 MB DOC)Click here for additional data file.Table S4Maternal plasma DNA analysis using Haplotype Ratio calculations.(0.06 MB DOC)Click here for additional data file.Table S5Informative tandem SNP assays and subject number.(0.03 MB DOC)Click here for additional data file.Table S6MLA followed by CTCE analysis. Starting from approximately 9 copies per tube  following MLA, approximately 47 copies were created per cycle or a yield greater than 500%.(0.06 MB DOC)Click here for additional data file.Text S1Supporting Information.(0.07 MB DOC)Click here for additional data file.


Copy number variants (CNVs) occupy a significant portion of the human genome and may have important roles in meiotic recombination, human genome evolution and gene expression. Many genetic diseases may be underlain by CNVs. However, because of the presence of their multiple copies, variability in copy numbers and the diploidy of the human genome, detailed genetic structure of CNVs cannot be readily studied by available techniques.Single sperm samples were used as the primary subjects for the study so that CNV haplotypes in the sperm donors could be studied individually. Forty-eight CNVs characterized in a previous study were analyzed using a microarray-based high-throughput genotyping method after multiplex amplification. Seventeen single nucleotide polymorphisms (SNPs) were also included as controls. Two single-base variants, either allelic or paralogous, could be discriminated for all markers. Microarray data were used to resolve SNP alleles and CNV haplotypes, to quantitatively assess the numbers and compositions of the paralogous segments in each CNV haplotype.This is the first study of the genetic structure of CNVs on a large scale. Resulting information may help understand evolution of the human genome, gain insight into many genetic processes, and discriminate between CNVs and SNPs. The highly sensitive high-throughput experimental system with haploid sperm samples as subjects may be used to facilitate detailed large-scale CNV analysis. The human genome harbors extensive structural variation Ability to study the genetic structures of CNVs may help understand the evolution of the human genome, gain insight into many genetic processes, and discriminate between CNVs and single nucleotide polymorphisms (SNPs). However, challenges in study of genetic structures of CNVs stem from multiple dimensions, including: (1) multiple CNV segments sharing a high degree of sequence identity; (2) similarity between allelic variants of SNPs and paralogous variants of CNVs; and (3) the diploidy of the human genome. Although some available technologies may be used for CNV detection, it is difficult to use these techniques to learn the genetic structures of CNVs. For detailed study, an experimental system capable of detecting minor sequence variation, discriminating between allelic variants and paralogous variants, determining CNV segment numbers of various kinds is needed.In contrast to SNPs which have two allelic variants differing by a single base, a CNV may have more than two “alleles” that are actually haplotypes differing in the number of paralogous segments in the human population . In manyet al.et al. is used only for reference and comparison.In the study by Fredman The net genotyping signal for a CNV is from all individual segments with complex behavior In the present study, haploid sperm samples were used as subjects. With these samples, allelic variants can be easily discriminated from paralogous variants because the former can only be detected from different sperm while the latter can be observed in a single sperm sample. Since CNV haplotypes are naturally segregated into different sperm cells during meiosis, genotype information from sperm samples is actually the information of individual haplotypes, making the study very simple in contrast to diploid cells for which genotype information is a mixture of information from two haplotypes. Furthermore, with single sperm, the haplotype composition in sperm donors and in the studied population, and paralogous variants in each haplotype can be easily studied.et al.Sixty-five markers characterized by Fredman et al. were used to discriminate between the allelic (for SNPs) or paralogous (for CNVs) variants for all markers. We genotyped all 65 markers in 189 single sperm cells from 11 unrelated Northern European donors by microarray after multiplex PCR amplification of all marker sequences in a single tube. The natural logarithm of the ratio, Ln(R), between the signal intensities of the two colors representing the two variants of each marker was plotted against the sum of these two signal intensities. The genotypes of the sperm donors and segregating groups of the sperm from each donor were then determined using the scatter plot and statistically confirmed by the Student's t-test.Single base substitutions used in Fredman The left panel of When an SNP is located in a CNV, the signal intensities for the segregating alleles in the single sperm samples display characteristics different from those of SNPs in unique regions. As shown in However, in contrast to the signals obtained from SNPs, the Ln(R) values of one of the two “alleles” for marker Rs2287968 fell into a range centered by −0.40. This cannot be explained by the behavior of an allele, and would be better understood as a CNV haplotype that was comprised of two paralogous variants , h1, m, and n under a given value of maxT which is an input number for the maximum number of T for computation, the 30 (or less when not available) least values of Si were recorded together with their corresponding T, h1, m, and n values. Because of the method is based on the expected and observed fractions, multiple solutions can be found for the variables with increments in proportion as T increases. We choose the group of values with Si immediately less than 1.00 as the “basic units.” They are so defined because the actual number of segments of each kind could be proportionally less or greater than the numbers of the basic unit. However, the ratios between the segment numbers of different variants in the haplotypes would remain constant.To find the minimum value of Using this method, our estimate of the total copy number of the paralogous segments for Rs2287968 is 10 in heterozygous individuals, and 3, and 7 in the two haplotypes, respectively. Haplotype 1 has 0, and 3 segments for the two paralogous variants, and in haplotype 2 had 2 and 5, respectively. Haplotype 1 contained 0 segments for one paralogous variant and 3 for the other while haplotype 2 contained 2 segments for one variant and 5 for the other.A CNV may have more than two haplotypes in the human population. When the number of haplotypes is large, the possible combinations of these haplotypes among human individuals could be very large. However, since individual haplotypes can be easily resolved by sperm analysis, CNV analysis is not complicated by the number of haplotypes or genotypes in the human population. The number and composition of each haplotype may be assessed using the above method. As shown in A main feature of a CNV is all or some of their haplotypes with Ln(R) values falling between those of the SNP alleles. As shown in Based on the microarray data, all 17 SNPs were shown to be true SNPs. Marker Rs624516, which was previously described as a PSV, was excluded for further analysis because of its poor signal intensities from majority of the samples. The numbers of haplotypes resolved for the remaining 47 markers are listed in To learn whether more haplotypes might be present in the human population, genotypes of additional 40 population samples from four ethnic groups see  were anaAs shown in The high degree of concordance between our experimental results and expectations based on meiotic segregation of the alleles/haplotypes has already demonstrated a high-level of accuracy and reliability of our system. To further prove its robustness, we used a different method and reanalyzed a subset of the samples and markers: four semen samples, D17, D18, #11 and AB012, and three markers, Rs2931178, Rs439825 and Rs440199 which were shown to have a single, two, and three haplotype(s) among the 11 sperm donors, respectively. No SNP marker was included in this round of study because the robustness of our system for genotyping of large panels of SNPs had been demonstrated in our previous publications in silico analysis of publicly available human genomic sequence. However, this strategy is limited by the (incomplete) representation of CNVs in the human genome assembly. Recent discoveries of abundant CNVs Identification and characterization of CNVs represents a substantial technological challenge. Our simple initial strategy to identify sequences that are likely not unique in the human genome was by PCR amplification in conjunction with gel electrophoresis might reveal CNVs if the lengths of the amplified sequences differ. Our screen of 65 markers with this method revealed CNV characteristics for only a small fraction (22.9%) of known CNVs. A clear pitfall with this method is its low specificity because nonspecific PCR products may be generated from other regions that are not target CNVs. In this case, no distinction could easily be made between CNV sequences and non-specific PCR amplicons based on size alone. Thus, gel electrophoresis of PCR products may not be an effective and reliable way to CNV screen.http://snp.cshl.org/about/qa.shtml). We assigned CNV status to 60% (29/48) of previously identified CNVs correctly based on a 95% confidence interval for the possible maximum heterozygosity. All the CNVs identified by this method were also confirmed by single sperm analysis. Although this method is more sensitive than the gel assay, 40% of the known CNVs in our set remained unidentified.The same set of 65 markers was also checked by genotyping DNA samples from the 40 human individuals from four different ethnic groups, a method commonly used to validate SNP to learn whether they are polymorphic or monomorphic, e.g. by TSC  are fully homozygous genomes, and were used for CNV study previously Results from the present study are not all consistent with those reported by Fredman et al. With emerging ability to assay single sperm with quantitative results, such as in an emulsion PCR, we could begin assess the frequency of duplication and deletion events in single molecules, giving further clues to which regions are prone to such CNVs, and to which extent they get transmitted across generations.In summary, we demonstrated that single-sperm typing is a very efficient and reliable way to learn great details of genetic structures of CNVs in the human genome, and may allow us to study genetic events occurring in the chromosomal regions containing these sequences.et al.Seventeen MSVs, 12 PSVs, 19 SIDs and 17 SNPs in unique sequences described in Fredman p = 0.95, T test; and p = 0.65, χ2 test).Single sperm samples were prepared by flow cytometry. Sperm DNA was released and prepared ready for multiplex amplification as described previously http://www.repeatmasker.org), all using standard parameters.We performed in silico search for evidence of non-unique character for all PCR amplicons by three different methods: (1) BLAT search against a non-redundant representation of the human genome 2, and 100 µg/ml gelatin), four dNTPs , primers (20 nM each) for all 65 markers included in the present study, 6 units of HotStart Taq DNA polymerase , and 5 ng of genomic DNA or DNA released from a single sperm. The samples were first heated to 94°C for 15 min to activate the Taq DNA polymerase followed by 40 PCR cycles. Each PCR cycle consisted of 40 sec at 94°C for denaturation and 2 min at 55°C followed by 5 min of ramping from 55°C to 70°C for annealing and extension. A final extension step was carried out at 72°C for 3 min at the end of the 40th cycle. PCR amplifications were performed with thermal cyclers capable of ramping as slow as 0.01°C/sec, including the PTC100 Programmable Thermal Controller (MJ Research), T3 Thermocycler (Biometra), and PxE Thermal Cycler (Thermo Electron). Single-stranded DNA (ssDNA) was generated by using a mixture of one primer from each pair and a small amount (1–2 µl) of the multiplex PCR product under the same conditions used for multiplex PCR. The resulting ssDNA was hybridized to an oligonucleotide probe microarray on a glass slide. The probes were designed in such a way that their 3′-ends were immediately next to the polymorphic sites in the ssDNAs, and were specifically extended with fluorescent-labeled dideoxyribonucleotides (ddNTPs) in the presence of Sequenase The genomic DNA samples and DNA from single sperm were subject to multiplex amplification followed by microarray analysis as described elsewhere Restriction enzymes, Fun4HI and DdeI, were purchased from the New England Biolabs . Single sperm samples were prepared manually using a procedure described previously


Macaca mulatta) are the primate most used for biomedical research, but phenotypic differences between Indian-origin and Chinese rhesus macaques have encouraged genetic methods for identifying genetic differences between these two populations. The completion of the rhesus genome has led to the identification of many single nucleotide polymorphisms (SNPs) in this species. These single nucleotide polymorphisms have many advantages over the short tandem repeat (STR) loci currently used to assay genetic variation. However, the number of currently identified polymorphisms is too small for whole genome analysis or studies of quantitative trait loci. To that end, we tested a combination of methods to identify large numbers of high-confidence SNPs, and screen those with high minor allele frequencies (MAF).Rhesus macaques (By testing our previously reported single nucleotide polymorphisms, we identified a subset of high-confidence, high-MAF polymorphisms. Resequencing revealed a large number of regionally specific SNPs not identified through a single pyrosequencing run. By resequencing a pooled sample of four individuals, we reliably identified loci with a MAF of at least 12.5%. Finally, we found that when applied to a larger, geographically variable sample of rhesus, a large proportion of our loci were variable in both populations, and very few loci were ancestry informative. Despite this fact, the SNP loci were more effective at discriminating Indian and Chinese rhesus than STR loci.Pyrosequencing and pooled resequencing are viable methods for the identification of high-MAF SNP loci in rhesus macaques. These SNP loci are appropriate for screening both the inter- and intra-population genetic variation. Macaca mulatta) are used more extensively as animal models for the study of human disease than any other primate species. They provide the primary model for research in infectious diseases, reproductive biology, behavior, neuroscience and immunology. More recently, they have been employed in research and vaccine development for the human immunodeficiency virus (HIV)  and itsEight of the approximately 23,000 candidate SNPs  were selMacaca mulatta individuals were selected for SNP confirmation. DNA was extracted from EDTA-preserved blood samples using the QIAmp Blood Mini Kit . All of these individuals  had previously been sequenced for 835 bp of mtDNA and classified into one of six regional haplotypes: IND1 (Indian); CHIE (Eastern China); CHIW1, CHIW2, and CHIW3 ; and CHIS (Southern China) . Each STR primer pair was optimized for annealing temperature and extension time: 94°C for 3 min., then 60 cycles of 94°C for 20 sec., 54–62°C (-0.1°C/cycle), 72°C for 45–90 sec., and a final extension at 72°C for 5–60 sec. All samples were analyzed on an ABI 310 sequencer using the Liz 500 size standard, and the ABI GeneScan software to assign genotypes. Some of the STR genotypes included in this study were also included in 4)2SO4, 0uded in [. Principuded in [ package Experiments conceived and designed by RSM, SK, and DGS. JAS and RYT performed the experiments. RSM, JAS, RYT and VSM analyzed the data. RSM and DGS contributed reagents, materials and analysis tools. JAS, RSM, SK and DGS wrote the paper.The file "Additional_File1.doc" has been uploaded. This file is a table in Microsoft Word format. The data is titled The file is titled: "Primer Information for Rhesus SNP Resequencing" and includes the name of the pyrofragment in which the SNP was located, the chromosome and nucleotide position, the direction of the change, the number of overlapping pyrofragments and the Phred score of the SNP.Click here for fileThe file "Additional_File2.doc" has been uploaded. This file is a table in Microsoft Word format. The data is titled: "All validated SNPs", and includes the chromosome, nucleotide position, the name of the 454 fragment in which the SNP was originally discovered, the polymorphism, the observed heterozygosity in the sample of Chinese animals, observed heterozygosity in the sample of Indian animals, the gene or feature in which the SNP is located (if any), the nearest genes or features at both the 5' and 3' sides (with a maximum distance of 1.5 Mb).Click here for file


An array-based SNP genotyping platform for Plasmodium falciparum is reported together with an analysis of SNP diversity in global population samples. Plasmodium falciparum exhibits abundant genetic diversity, and this diversity is key to its success as a pathogen. Previous efforts to study genetic diversity in P. falciparum have begun to elucidate the demographic history of the species, as well as patterns of population structure and patterns of linkage disequilibrium within its genome. Such studies will be greatly enhanced by new genomic tools and recent large-scale efforts to map genomic variation. To that end, we have developed a high throughput single nucleotide polymorphism (SNP) genotyping platform for P. falciparum.The malaria parasite Using an Affymetrix 3,000 SNP assay array, we found roughly half the assays  yielded high quality, 100% accurate genotyping calls for both major and minor SNP alleles. Genotype data from 76 global isolates confirm significant genetic differentiation among continental populations and varying levels of SNP diversity and linkage disequilibrium according to geographic location and local epidemiological factors. We further discovered that nonsynonymous and silent (synonymous or noncoding) SNPs differ with respect to within-population diversity, inter-population differentiation, and the degree to which allele frequencies are correlated between populations.P. falciparum, and that many of these changes may reflect functional variants deserving of follow-up study. Our analysis demonstrates the potential for new high-throughput genotyping technologies to enhance studies of population structure, natural selection, and ultimately enable genome-wide association studies in P. falciparum to find genes underlying key phenotypic traits.The distinct population profile of nonsynonymous variants indicates that natural selection has a significant influence on genomic diversity in  Plasmodium falciparum is the most virulent species of malaria and the primary cause of malaria-related mortality across the globe. The success of P. falciparum as a pathogen derives in part from its high levels of genetic diversity [P. falciparum will improve understanding of these capabilities, and will permit wide-ranging investigation of the parasite's biology, including population structure and history, outcrossing and recombination frequency and instances of natural selection, and inform effective intervention strategies. As a target for genotyping, P. falciparum has the advantage that it is haploid during human stages of its life cycle, making identification of haplotypes and inference of outcrossing from patient isolates more straightforward than in primarily diploid parasites.iversity -4, diveriversity , to thwaiversity , and to iversity -9. LargePrior multilocus analyses have revealed important aspects of the parasite's biology even though these studies did not encompass the entire genome. Microsatellite studies, the largest using 342 loci, have revealed geographic differences in genetic diversity and linkage disequilibrium (LD) that are correlated with the incidence of multiple infections, with diversity highest in African populations and lowest in South American populations , as wellP. falciparum have uncovered tens of thousands of new SNPs, indels, and structural variants [P. falciparum. The first step needed is to develop high-throughput genotyping technology that can take advantage of the extensive new diversity data.Recent genome-wide surveys of genomic variation in variants -4,13. ThP. falciparum genome, with mixed parasite genotypes, and with human DNA contamination. Using SNP data collected from the array, we confirm with greater resolution differences in the level of genomic diversity between African, Asian, and American populations of P. falciparum, document genetic differentiation of those continental populations as well as the presence of structure within non-African populations, and explore the important roles of natural selection and recombination in sculpting genetic variation within malaria populations. We anticipate that a scaled-up genotyping array based on this now-proven technology will usher in fundamental new insights into basic malaria biology as well as novel disease intervention strategies.In this manuscript we report the development of a 3,000 SNP Affymetrix genotyping array and initial biological observations resulting from its deployment with 76 globally distributed parasite isolates. We demonstrate the ability of this platform to contend with the extremely high (approximately 81%) A/T composition of the P. falciparum genome. We included 2,153 SNPs from chromosome 7 on the array to assess patterns of LD near a known selective sweep [P. falciparum strains we used for SNP ascertainment [P. falciparum genome. A number of assays consistently yielded only the major (most common) SNP allele, suggesting either that those assays were faulty or that the genomic positions the assays targeted were incorrectly identified as polymorphic. Removal of these uninformative assays as well as assays that gave an incorrect genotype call for one or more samples yielded a set of 1,638 assays that achieved perfect concordance with known genotypes for both the major and minor alleles in the 16 samples used for SNP ascertainment. A histogram of DM, BRLMM, and BRLMM-P call rates for this validated set of 1,638 SNPs in the ascertainment sample set is illustrated in Additional data file 1. Replicate hybridization data (not shown) for the HB3 and 3D7 samples indicate that overall genotyping consistency is 99.9%. Results of experiments to test array performance in the presence of human DNA or DNA from mixed malaria infections are included in Additional data files 2-4.The genotyping array employs a standard Affymetrix 500 K array design, utilizing a total of 56 probes arrayed in 14 quartets to interrogate each SNP. Mismatch probes were utilized in order to evaluate the effectiveness of the Dynamic Modeling (DM) calling algorithm for the A/T-rich ve sweep , as wellP. falciparum genome. This collection is summarized in Table P. reichenowi, the species most closely related to P. falciparum, in order to root allelic dimorphisms. All analyses were performed using a set of 1,441 assays  that achieved a call rate of at least 80% among all 76 geographic or SNP ascertainment samples. A total of 874 assays of this subset of 1,441 come from chromosome 7; 281 SNPs are intergenic, 48 are intronic, 386 are synonymous coding, and 726 are nonsynonymous. Assayed genic SNPs occur in 180 genes on chromosome 7 and 487 genes from all 13 of the remaining chromosomes. The genomic coordinates (using the 3D7 PlasmoDB 5.0 assembly as reference) of all 1,441 assays used in downstream analyses are included in Additional data file 5, along with genotype information for all 76 geographic or SNP ascertainment samples. These genotyping data have also been submitted to PlasmoDB [We hybridized a global collection of parasite isolates to the array to examine population structure, SNP diversity, and LD in the PlasmoDB .Overall SNP diversity at assayed loci varied by geographic region. Diversity was quantified using a statistic we term 'SNP π', defined as the average proportion of pairwise differences at assayed SNP loci within a defined population. At the continental scale, we measured the following SNP π values across all SNPs, with 95% confidence intervals indicated in parentheses: Africa = 0.234 (0.224-0.244); Asia = 0.227 (0.219-0.236); Americas = 0.14 (0.130-0.147). This ranking agrees with observations made using microsatellites , but difpfcrt on chromosome 7. We assayed the genotyped strains for resistance to chloroquine, and divided the Asian and African samples into chloroquine-resistant (CQR) and chloroquine-sensitive (CQS) groups. Separately calculating the SNP π values for each group, we found a clear signal of selection ; P < 0.01) in the region of reduced diversity (located at approximately 460 kb) around pfcrt  and Malawi isolates (sample prefix = CF) cluster together, although the clade they comprise does not exhibit strong bootstrap support. The HB3 and Santa Lucia samples  are allied with the Brazilian samples, but curiously they also cluster strongly with the Senegal sample SenP26.04. SenP26.04, as well as other samples that do not cluster according to their sampling location  may represent strains with poor phylogenetic signal, cases of recent migration, or instances of cross-contamination in culture. The longer terminal branches of the Senegal isolates relative to the Brazilian or Asian isolates reflect the greater genomic diversity in that population, which is consistent with the higher rates of outcrossing and prevalence of infection observed in African populations.We examined the relatedness of the parasite isolates, and present a maximum likelihood phylogenetic tree rooted with SNP ascertainment bias can influence phylogenetic analyses if a limited number of reference strains are used to identify SNPs . We perfP. reichenowi is excluded). This abundance of singletons derives partially from bias in SNP ascertainment; 3D7 has been more deeply sequenced  allele contribute to the long terminal branch length of 3D7, while 3D7 singletons with the ancestral (P. reichenowi) allele artificially enhance the evolutionary affinity of 3D7 and P. reichenowi.The genomic reference strain 3D7 exhibits the longest branch of any ingroup taxon in the phylogenetic tree, resulting from the presence of 83 singleton SNPs in this lineage (142 if coverage ) than anWe further analyzed the SNP data with two other population genetic analysis software packages, the programs Structure (v2.2.2) and SmartPCA. Structure uses a Bayesian approach to calculate the posterior probability of the number of populations sampled by a multilocus genotype dataset . For ourpfcrt selective sweep on chromosome 7 resulted in a heavy loading of signal in that region (data not shown), so SNPs from that region were excluded from the present analysis. Principal components analysis suggests two additional conclusions. First, it provides evidence for structure within the Brazilian population, consistent with a previous report [P. falciparum was introduced independently into the two regions, but it does suggest that there has been little gene flow between them.We used SmartPCA  to perfos report  . All pairwise population comparisons are significant by bootstrapping at P < 10-4, but the magnitude of FST varies considerably among the comparisons. We found the greatest differentiation between Asian and American samples (FST = 0.431), followed by Africa versus Americas (0.306), and Africa versus Asia (FST = 0.236). The much greater genetic differentiation between American and Asian P. falciparum populations than American and African populations  supports a hypothesis of recent colonization of the Americas by African P. falciparum strains after the arrival of Europeans.Analysis of population divergence using the FST for nonsynonymous SNPs relative to silent SNPs  across Senegal and Thailand to control for this effect indicates that nonsynonymous SNPs still exhibit greater population structure than silent SNPs  between the Senegal and Thailand populations is inversely correlated with P. falciparum diversity (as measured worldwide) at the same locus (Pearson's ρ = -0.17); high FST SNPs are, in fact, almost absent from high diversity regions . An additional factor, also related to selection, is probably operating here as well: loci that have experienced population-specific selective sweeps will have higher divergence with other populations as well as reduced diversity in that population, which will be reflected in the global diversity. Differential intensity of purifying selection between populations would be unlikely to yield a similar negative regional association between diversity and divergence, as relaxed purifying selection in a population would be expected to simultaneously and uniformly increase both divergence and diversity. Whatever the particular mechanism, the correlation illustrates the important role selection has in shaping population divergence in P. falciparum.The importance of selection in shaping P = 0.03, Thailand P = 0.05). This may be indicative of purifying selection maintaining slightly deleterious amino acid replacements at low population frequencies. The nonsynonymous and silent DAF spectra for Thailand show an abundance of high-frequency (90-99%) derived SNPs relative to Senegal. An excess of high-frequency derived alleles is an expected byproduct of genetic hitchhiking [Figure chhiking , wherebychhiking .ST metric , these 3 strains (out of 23 in the population sample) exhibit an ancestral allele for approximately two-thirds of high DAF SNPs in both the nonsynonymous and silent data sets. Such a non-uniform distribution of ancestral alleles is unexpected from a freely mixing population. We conclude that hidden population structure, perhaps exacerbated by reduced outcrossing in Thailand P. falciparum populations, is responsible for the contrast in DAF spectra between Senegal and Thailand.Alternatively, the excess of SNPs exhibiting high DAF in Thailand could result from hidden population structure in the sample, where a derived allele is precluded from being classified as 'fixed' (100% frequency) by the inclusion in the sample of a few isolates belonging to a distinct population where the derived allele has not fixed. If this were the cause of the excess of high frequency derived alleles, we would expect the same few samples to exhibit an ancestral allele at multiple loci with high DAF. We find evidence to support this hypothesis. Though the Thailand samples T116, PR145, and TD257 are not phylogenetically distinct from the other Thailand samples . Both nonsynonymous and silent derived allele frequencies are susceptible to bias from hidden population structure or other departures from demographic equilibrium, so the contrast between these two classes of SNPs is most likely due to population-specific natural selection.Though hidden population structure and SNP ascertainment bias may confound direct interpretation of DAF spectra in Senegal and Thailand, the DAF correlation between these two populations is more directly indicative of natural selection. Nonsynonymous SNPs exhibit a significantly weaker DAF correlation between Senegal and Thailand than silent SNPs (rP = 6.34 × 10-4). We find 55 nonsynonymous SNPs with frequency deviations above this threshold compared to only 13 synonymous mutations, from an original set of 598 nonsynonymous SNPs and 598 (by coincidence) silent SNPs that were polymorphic in one or both of these populations. By simple inference, then, 55 - 13 = 42 (7%) of the nonsynonymous SNPs in this class may be subject to selection, as represented by the dashed line in Figure P = 1.02 × 10-6), suggesting that even minor differences in nonsynonymous SNP allele frequencies among parasite populations may reflect the influence of natural selection.We can roughly estimate the proportion of nonsynonymous SNPs subject to differential positive or negative selection as a function of the disparity in derived allele frequency between Senegal and Thailand. If nonsynonymous and silent SNPs are subject to equal evolutionary pressures, demographic events, and ascertainment biases, then similar proportions of these two classes of SNPs should exhibit differences in allele frequency between the two populations that are above or below a threshold value. Deviations from this expectation are measurable by a Fisher's exact test applied to a 2 × 2 contingency table containing counts of the number of SNPs in each class above or below the threshold frequency disparity. The solid line in Figure pfcrt gene), where our SNPs are concentrated. Removing that region  did reduce r2, but only at very short distances and only by approximately 5%, suggesting that the selective sweep does not significantly bias allele frequencies otherwise analyzed. Assuming there has been no recent large-scale admixture in Thailand and Brazil, the longer LD can be attributed to their smaller effective population size and the reduced outcrossing in low-transmission locales.Analysis of LD using the full set of genotyped SNPs reveals striking variation in the extent of LD across different populations. A greater extent of LD in Asian than in African populations has previously been reported ,10,12, aP < 10-4) amount of LD between unlinked markers, as determined by calculating r2 between markers lying on different chromosomes. Correlation between alleles at unlinked markers is evidence for population structure within the data sample. Since accidental inclusion of closely related samples can produce the same effect, we removed one sample from the most closely related pair of Thai samples and redid the calculation; the change to measured LD was negligible. Population structure has previously been reported within South America [The two non-African samples also showed a significant , which suggests a role for natural selection in the ongoing genetic differentiation of malaria populations in disparate geographic locales. Though a previous investigation observed a similar contrast in divergence between ten nonsynonymous SNPs at loci known to be associated with anti-malarial drug resistance and ten silent SNPs at housekeeping or unknown loci [The results we present here on malaria population biology are important in that they confirm with significantly more data previous observations about the distribution of diversity in the own loci , we haveP. falciparum .Both negative (purifying), and positive  selection can increase population divergence if the efficacy of the selection differs geographically . A recenP. falciparum populations, there are numerous potential population-specific environmental factors that could induce differentiation by positive selection. For example, population differentiation at nonsynonymous SNPs could result in variation among human host populations in HLA allelic composition, different Anopheles mosquito vectors, and/or heterogeneous application of anti-malarial therapeutics. Alternatively, some of the variant nonsynonymous SNPs may represent positively selected mutations that have arisen due to selection pressures that are present in both populations, but which have been prevented from spreading between the populations due to geographic barriers to gene flow. Deeper probing of these questions with expanded sample sets and denser SNP assays should illuminate whether these observed patterns of divergence and polymorphism are due to negative, positive, or balancing selection pressures.Even if negative selection plays an important role in nonsynonymous SNP divergence among P. falciparum populations warrant deeper follow-up investigations, as many may represent functional variations with important implications for public health or understanding of basic malaria biology. Nonsynonymous SNPs that differ even marginally in frequency among populations may be subject to selection , but instead that it reflects many independent, relatively unbiased local profiles of the interplay of natural selection and genetic drift.The SNPs we profiled, consequently, may be expected to yield a conservative estimate of the degree to which segregating genetic variation in as AMA-1 ,27 or MS1 [MSP-1  that areP. falciparum, underscore the extreme variability of the population biology and epidemiology of malaria [P. falciparum, likely as a result of greater incidence of infection (population size) and simultaneous infection of single hosts by multiple parasite lines . Consideration of such population factors will be important in interpreting observations of SNP diversity and selecting appropriate locations for genome-wide association studies, which require LD between SNP markers and causal loci for success. For example, if resistance to an anti-malarial drug arises in an Asian or American P. falciparum population, strong selection and low rates of outcrossing will likely leave a strong signal in the SNP diversity within those populations detectable through haplotype-based tests [Our observations of large disparities in SNP diversity levels and LD magnitude, as well as significant selection-driven genetic differentiation among geographic populations of  malaria ,10,12. Oed tests .P. falciparum genome, and have used a pilot version of this platform to perform the most comprehensive genome-wide genotyping analysis to date in this parasite. We are developing an expanded version of this array that will use probes to survey approximately 75,000 P. falciparum SNPs, as well as tiled probes to detect known and novel polymorphisms in dozens of other malaria genes of interest. We expect this high-density, genome-wide multilocus genotyping approach to have broad utility for investigations of basic malaria biology as well as for public health applications such as diagnostics and surveillance for drug resistance. Population genetic analyses that exploit regional variation in diversity or LD across the genome have already proven useful for re-identifying known drug-resistance loci [In summary, we have demonstrated the effectiveness and efficiency of the Affymetrix SNP genotyping array as a platform for high-throughput genotyping in the AT-rich nce loci ,30-32, apfcrt locus [A standard Affymetrix array design was utilized, with each SNP interrogated by a set of fifty-six 25-mer nucleotide probes arranged in fourteen probe quartets . SNPs identified through comparative sequencing  were selrt locus . The remDNA was isolated from culture-adapted parasites (see 'Parasite samples' below) using standard 100/G protocols  and resuspended in TE (Tris EDTA) buffer for processing and hybridization. Genome-wide genotype data from the Affymetrix array were generated with the human 500 K array protocol but with a smaller hybridization volume of 125 μl owing to the smaller surface area of the malaria array.P = 0.05 for BRLMM-P, 0.50 for BRLMM, 0.30 for DM). Genotype calls for 16 reference arrays were then compared against known sequencing data for the same strains [Haploid SNP calls were made using several algorithms available in the Affymetrix Power Tools (apt-1.8.5): DM, BRLMM, and BRLMM-P. Default confidence thresholds were employed for all algorithms  or additional sources noted Table . MR4 isoST statistic, calculated with the method of Hudson, Slatkin and Maddison [ST and SNP π comparisons, as well as 95% confidence intervals for both statistics, were calculated using nonparametric bootstrapping. We also employed Structure 2.2 software [All population genetic analyses were conducted using SNPs that yielded 100% accurate genotyping results for both the major and minor assayed alleles, and which yielded calls in at least 80% of the population samples hybridized to the array. Measurements of population diversity were carried out using the 'SNP π' statistic, which was calculated as the average number of pairwise differences at assayed SNPs between all members of a sample. Population divergence was measured using the FMaddison . Statistsoftware  to examisoftware . For thiThe maximum likelihood phylogenetic analysis was conducted using PHYML software  on a BIOPopulation-specific DAF spectra are based on only those SNPs that are not fixed in a given population to reduce spectrum distortion from SNPs that were born in another population following. Consequently, slightly different SNP sets are used in each population. DAFs were estimated for 360 nonsynonymous and 399 silent SNPs in Senegal, and 397 nonsynonymous and 387 silent SNPs in Thailand.2 was calculated within each population for all pairs of SNPs sharing the same chromosome; pairs were binned by distance and averaged within each bin. The level of LD between unlinked markers was estimated by calculating r2 between all pairs of SNPs on different chromosomes. To determine the bias caused by small sample size, the unlinked calculation was repeated, with the change that for each pair of SNPs, the genotype for one was taken from one strain while the genotype for the second was taken from another strain. This background value of r2 was calculated separately for the possible pairs of different strains and then averaged. The statistical significance of the observed LD between unlinked markers was estimated by calculating r2 under bootstrap re-sampling of unlinked markers; this provides an estimate of the statistical uncertainty on unlinked r2 and an overestimate of the uncertainty on background LD. In 10,000 permutations, the unlinked value was always much higher (>10 standard deviations) than the background value.LD was studied within each of the four populations with enough samples: Thailand, Malawi, Senegal and Brazil. For this analysis, two nominally Thai samples (T2/C6 and TM-4C8-2) were dropped that clustered with other populations in the phylogenetic analysis, and only parasites representing single genomes were included in the analysis. Missing genotypes were not imputed. The widely used LD statistic rP. falciparum strains against antimalarial drugs in clinical use. Parasites selected for the assays were derived from the same isolates that furnished DNA for the genotyping studies. Parasites were grown and sorbitol-synchronized to the ring stage by standard methods, and 72-hour drug responsiveness was measured using a DAPI DNA-staining assay in a 384-well plate format as previously described [pfcrt.Chloroquine diphosphate-sensitivity assays were performed as part of an ongoing screen of established escribed . Dose-reBRLMM: Bayesian Robust Linear Modeling using Mahalanobis Distance; DAF: derived allele frequency; DM: Dynamic Modeling; LD: linkage disequilibrium; MAF: minor allele frequency; SNP: single nucleotide polymorphism.DEN and SFS designed experiments, performed population genetic analyses and wrote the paper. SKV designed experiments, prepared samples, and wrote the paper. DP performed SNP calling and analysis of raw genotyping data. PM supervised SNP calling and raw data analysis. DAM and AL helped with parasite cultures and consulted on project outcomes. DR helped with parasite culture. RD extracted and prepared DNA for hybridization. NH and CG hybridized samples to the array. JFC and ET performed drug phenotyping assays. NS-T created DNA libraries. OS, DN, ON, SM, MF, SM, AD, and CC helped with sample collection. RCW coordinated project flow and supervised data collection. DLH consulted on population genetic analysis. BWB supervised and advised on data collection. ESL consulted on project outcomes. PCS designed experiments, consulted on population genetic analysis and wrote the paper. DFW designed experiments, coordinated all efforts, supervised the project at all levels, consulted on project outcomes and wrote the paper. All authors read and approved the final manuscript.ST (bootstrapping P < 0.05), controlling for average derived allele frequency in Senegal and Thailand. Additional data file The following additional data are available with the online version of this paper. Additional data file Lines indicate the number of SNPs exhibiting various call rates using the DM, BRLMM, and BRLMM-P SNP calling algorithms. BRLMM-P SNP calls were used for analysis.Click here for fileArray performance in the presence of human DNA and malaria DNA from mixed  infections.Click here for fileReported results are for SNP loci known to exhibit different alleles between the HB3 and Dd2 lines. The highest proportion of heterozygous calls was observed for the even (1:1) mixture of malaria.Click here for fileConcordance with known genotype can be improved using more stringent confidence cutoff values with the BRLMM-P calling algorithm.Click here for fileGenomic location and genotype data for SNPs assayed on the array with a call rate of at least 80%.Click here for file(a) High MAF (MAF > 0.25) topology. (b) Low MAF (MAF < 0.25) topology. Nodes exhibiting bootstrap support levels of at least 50% or 90% are indicated by gray dots and black dots, respectively. Bootstrap support and branch length differ between the topologies, but the American and Asian parasites form congruent clades.Click here for file(a) Plot of the likelihood of the genotyping data given that the samples derive from K = 1-5 populations. (b) Plot of the posterior probability of population membership for each sample hybridized to the array, assuming three underlying populations.Click here for file(a) First two principal components for Brazil data; clusters suggest population structure. (b) First two components for worldwide data set. (c) First and third components for worldwide data set.Click here for fileP < 0.001.Significantly greater nonsynonymous divergence (determined by bootstrapping) is indicated by asterisks: **Click here for fileNonsynonymous and silent SNP DAF correlation between Senegal and Thailand.Click here for file


Microarray gene expression data has been used in genome-wide association studies to allow researchers to study gene regulation as well as other complex phenotypes including disease risks and drug response. To reach scientifically sound conclusions from these studies, however, it is necessary to get reliable summarization of gene expression intensities. Among various factors that could affect expression profiling using a microarray platform, single nucleotide polymorphisms (SNPs) in target mRNA may lead to reduced signal intensity measurements and result in spurious results. The recently released 1000 Genomes Project dataset provides an opportunity to evaluate the distribution of both known and novel SNPs in the International HapMap Project lymphoblastoid cell lines (LCLs). We mapped the 1000 Genomes Project genotypic data to the Affymetrix GeneChip Human Exon 1.0ST array (exon array), which had been used in our previous studies and for which gene expression data had been made publicly available. We also evaluated the potential impact of these SNPs on the differentially spliced probesets we had identified previously. Though the 1000 Genomes Project data allowed a comprehensive survey of the SNPs in this particular array, the same approach can certainly be applied to other microarray platforms. Furthermore, we present a detailed catalogue of SNP-containing probesets (exon-level) and transcript clusters (gene-level), which can be considered in evaluating findings using the exon array as well as benefit the design of follow-up experiments and data re-analysis. Gene expression is an intermediate phenotype that resides between DNA sequence variation and higher-level cellular or whole-body phenotypes including disease susceptibility and individualized drug response. Whole genome expression profiling using high throughput microarray platforms has been a powerful tool used by investigators to create a global picture of cellular function through the quantitative measurement of mRNA of thousands of genes in parallel. Over the past decade, more than 3,000 scientific publications have reported results using the Affymetrix GeneChip arrays  alone, one of the most frequently used microarray platforms for expression profiling http://www.hapmap.org/) et al. et al. measured and compared ∼18,000 gene-level transcript clusters A typical gene expression profiling study often involves measuring and comparing the relative amount of mRNA expressed in two or more experimental conditions . Notably, several recent studies using the International HapMap Project (cis-acting expression quantitative trait loci or eQTLs due to polymorphisms in probes Although appropriate preprocessing approaches such as the RMA (robust multichip average) http://www.1000genomes.org/), ambitiously aims to provide a most detailed map of human genetic variation through genotyping at least 1000 human genomes from world-wide populations using the next-generation sequencing technologies http://www.1000genomes.org/bcms/1000_genomes/Documents/1000Genomes-MeetingReport.pdf). In this study, we present a detailed characterization of SNP-containing probesets (exon-level) and transcript clusters (gene-level) of the exon array using the newly released 1000 Genomes Project genotypic dataset (http://www.scandb.org/) http://www.pacdb.org/) To date, the publicly available HapMap I/II genotypic data  dataset . We depoAn overview of the methods and major results is provided in The exon array uses a set of short 25-mer probes to target each feature of interest, together referred to as a probeset. The majority of exon array probesets contain four probes. A gene-level transcript cluster may have one or more exon-level probesets. In total, ∼1.4 million probesets and ∼5.4 million probes under the Affymetrix groupings of “core”, “extended” and “full” were included in our analysis. These probesets cover ∼18,000 “core” transcript clusters, which have RefSeq-supported Common SNPs were those with MAF  greater than 5%. Using the 1000 Genomes Project genotypic data, in total, 510,957  SNPs in the CEU samples and 783,071  SNPs in the YRI samples  , among the ∼1.4 million probesets on the exon array, 242,428 probesets (∼17%)  in the 57 CEU samples and 293,920 probesets (∼21%)  in the 56 YRI samples were found to contain SNPs . Supplemhttp://www.ncbi.nlm.nih.gov/projects/geo/). To evaluate their potential impact on the previous gene expression data, we compared our new list of SNP-containing probesets and transcript clusters derived from the 1000 Genomes Project with the ∼350,000 affected probesets from the SNPinProbe database (dbSNP v129). The probesets were further grouped based on the number of SNPs in affected probes . Compared with our previous SNPinProbe database We previously built a database of SNP-containing probesets in the exon array  may influence hybridization of target mRNA to microarray probes, thus causing unreliable measurements of expression intensities. Many of these technical factors can be optimized or controlled. However, due to the existence of genetic variation among individuals, polymorphisms in some samples may cause less efficient hybridization to microarray probes, which are often designed based on the reference sequences. Though a couple of recent studies considered the effect of polymorphisms in probes before summarizing expression data Focusing on common SNPs with MAF>0.05, overall, ∼17% and ∼21% probesets out of the total ∼1.4 million probesets were found to contain at least one common SNP in their probes for the CEU and YRI samples, respectively . The YRIConceptually, the 1000 Genomes Project genotypic data aims to cover both known SNPs and novel SNPs. Our next aim was to evaluate how the 1000 Genomes Project genotypic data was compared with the known dbSNP data. Particularly, we previously built the SNPinProbe database ARMC10 together with probesets 3017094, 3017095, and 3017088, has a novel SNP (chr7: 102526628) in the CEU samples  when the probeset 3017096 is included, but shows no evidence  when the SNP-containing probes are excluded.To further illustrate the potential effect of any probesets affected by novel SNPs as well as to demonstrate an application of this new resource of SNP-containing exon array probesets, we examined a list of 782 differentially spliced probesets between the CEU and YRI samples  samples . The ide samples  though, In summary, due to the amount of targets on a single microarray, obviously, a systematic experimental evaluation of the effects of polymorphisms on gene expression would be extremely difficult. Though statistical approaches may be used to adjust these effects in certain cases http://www.affymetrix.com/analysis/index.affx) (NetAffy build 27). The human genome reference version is NCBI build 36 .The probe, probeset (exon-level) and transcript cluster (gene-level) annotations for the Affymetrix GeneChip Human Exon 1.0ST array were downloaded from the Affymetrix NetAffx Analysis Center (ftp://ftp-trace.ncbi.nih.gov/1000genomes), as well as FASTA files for the human genome reference assembly (ftp://ftp.ensembl.org/pub/current_fasta/homo_sapiens/dna/). We wrote our own Extractor (for the FASTQ files) and Analyzer (for summarization), and invoke the tool  for the (binary) Sequence Alignment/Map format  used by the Sanger Institute. Summary analysis data are stored in a relational data store .The 1000 Genomes Project was launched in January, 2008. The first set of SNP calls of four genomes of HapMap LCLs (3 samples from a CEU parents-child trio and 1 YRI sample) were released Dec., 2008 from the high coverage (>20×) pilot. The SNP calls on the CEU trio  were based on the Illumina platform (mostly paired end 35 bp reads). The SNP calls on the YRI sample (NA19240) were detected using the Applied Biosystems SOLiD (Sequencing by Oligo Ligation and Detection) sequencing platform. More recently, sequence data and SNP calls on >600 genomes  in the low coverage pilot were also released. We downloaded the 1000 Genomes Project data of the currently available 57 CEU samples and 56 YRI samples including FASTQ files , SNP calls, and Binary Simple Alignment/Map files (BAM) . This facilitated the annotation of known SNPs with rs identifier (dbSNP v129), RefSeq http://www.ncbi.nlm.nih.gov/geo/). To evaluate the potential influence of any novel SNPs on our previous results, we compared the 782 differentially spliced probesets with our newly identified SNP-containing probesets. Particularly, probesets affected by novel SNPs in only one population (either CEU or YRI) were compared with the 782 probesets, assuming a comparison between the CEU and YRI samples could be especially biased due to the existence of SNPs in one population.We previously identified a list of 782 differentially spliced probesets between the CEU and YRI samples Figure S1Chromosomal distribution of affected core-level probesets in the exon array. Blue indicates the CEU samples; Red indicates the YRI samples. Common SNPs (MAF>0.05) are included. MAF: minor allele frequency.(1.41 MB EPS)Click here for additional data file.Table S1Chromosomal distribution of all SNPs in the exon array.(0.02 MB XLS)Click here for additional data file.Table S2Functional annotations of all SNPs in the exon array.(0.02 MB XLS)Click here for additional data file.Table S3SNP-containing probesets based on the 1000 Genomes Project genotypic data.(0.01 MB XLS)Click here for additional data file.Table S4A list of SNP-containing probesets (exon-level) for the CEU samples.(1.30 MB TXT)Click here for additional data file.Table S5A list of SNP-containing probesets (exon-level) for the YRI samples.(1.02 MB TXT)Click here for additional data file.Table S6Chromosomal distribution of probesets with common SNPs.(0.01 MB XLS)Click here for additional data file.Table S7The breakdown of affected probesets under the Affymetrix reliability groupings .(0.01 MB XLS)Click here for additional data file.Table S8The number of probesets containing probes with novel and common SNPs within each transcript cluster (CEU).(0.49 MB TXT)Click here for additional data file.Table S9The number of probesets containing probes with novel and common SNPs within each transcript cluster (YRI).(1.32 MB TXT)Click here for additional data file.Table S10Probe-level information of probesets containing novel SNPs (CEU).(2.46 MB TXT)Click here for additional data file.Table S11Probe-level information of probesets containing novel SNPs (YRI).(2.42 MB TXT)Click here for additional data file.Table S12The differentially spliced probesets that could be affected by novel SNPs.(0.00 MB TXT)Click here for additional data file.


The Minimum Error Correction (MEC) is an important model for haplotype reconstruction from SNP fragments. However, this model is effective only when the error rate of SNP fragments is low. In this paper, we propose a new computational model called Minimum Conflict Individual Haplotyping (MCIH) as an extension to MEC. In contrast to the conventional approaches, the new model employs SNP fragment information and also related genotype information, thereby a high accurate inference can be expected. We first prove the MCIH problem to be NP-hard. To evaluate the practicality of the new model we design an exact algorithm (a dynamic programming procedure) to implement MCIH on a special data structure. The numerical experience indicates that it is fairly effective to use MCIH at the cost of related genotype information, especially in the case of SNP fragments with a high error rate. Moreover, we present a feed-forward neural network algorithm to solve MCIH for general data structure and large size instances. Numerical results on real biological data and simulation data show that the algorithm works well and MCIH is a potential alternative in individual haplotyping. The availability of complete genome sequence for human beings  makes italleles. Almost all SNPs have two different alleles which we denote the wild type as 1 and the mutant type as −1. The SNP sequence information on each copy of a pair of chromosomes in a diploid genome is called a haplotype which is a string over {−1, 1}. A genotype is the conflated information of a pair of haplotypes on homologous chromosomes. For a genotype, if a pair of alleles at a SNP site is made of two identical values, this SNP site is called homozygous, otherwise it is called heterozygous.The nucleotides in a SNP position are called Haplotypes generally have more information content than that individual SNPs have in disease association studies , but it Computationally, the individual haplotyping problem is to determine the “best” pair of haplotypes from data (SNP fragments) which is possibly inconsistent and contradictory. It focuses on partitioning SNP fragments into two sets according to SNP states, with each set determining a haplotype . For sucThere are two ways to show that a new established model is practical or more effective than an existing model: one is to theoretically prove that the solution of the new model is superior to that of the existing model, the other is to numerically solve the problem and compare the solutions obtained by the two models. It is obvious that we can only do it in the second way. For this reason we try to design an exact algorithm for the MCIH problem. A special data structure suited for a dynamic programming algorithm is displayed and used to evaluate the model. Computational results by this exact algorithm confirm the significance of MCIH. Moreover, a feed-forward neural network (FNN) is designed for computing approximate solutions to the problem (MCIH) in general case and of large size. Extensive computational results show the effectiveness of the proposed FNN algorithm and MCIH.The paper is organized as follows: In Section I, we give the problem definition together with the complexity analysis of MCIH. The model evaluation is shown in Section II. In Section III, a feed-forward neural network algorithm is designed for implementing MCIH. Experiment results are given in Section IV. Section V concludes the paper.g = , if the jth SNP site is wild type homozygous, gj = 2; if it is mutant type homozygous, gj = −2; when it is heterozygous, gj = 0. A pair of haplotypes h1 =  and h2 =  is called compatible with a genotype g if the following conditions hold: for each SNP site j where gj = −2, h1j = h2j = −1; for each SNP site j where gj = 2, h1j = h2j = 1; for each SNP site j where gj = 0, h1j = −h2j = −1 or h1j = − h2j = 1.For a genotype m SNP fragments from a pair of chromosomes and the length of each corresponding haplotype is n. Define an m × n SNP matrix M = (mij), whose entry mij has the value −1, 1 or 0 . Each row mi corresponds to a SNP fragment fi and each column corresponds to a SNP site. Since the given SNP fragments may have different lengths, but are generally less than n, we also assign value 0 to the uncovered elements in a row.Suppose that there are x,y ∈ {−1, 1, 0} and defineLet fi =  and fk =  is defined as HD = ∑j=1nd. If HD > 0, we say two fragments fi and fk are in conflict, otherwise we call them compatible. HD > 0 indicates that either fi, fk are not from the same chromosome copy or there are errors in the data. HD is similar to the Hamming distance, i.e., the number of mismatches (conflicts) between two fragments. The distance between a fragment and a haplotype is defined in the similar way.then the distance between two SNP fragments The MEC problem  is definGiven a set of SNP fragments (a SNP matrix M) from an individual’s DNA and the related genotype g, reconstruct a pair of haplotypes compatible with g and involving a minimum number of conflicts with the given SNP fragments.MCIH : MCIH is in fact to correct a minimum number of SNP states under the guidance of the genotype information so that the modified SNP fragments can be divided into two disjoint sets of pairwise compatible fragments which determine a pair of haplotypes compatible with the genotype. The computational complexity of the MCIH problem (similar spirit with ) is discThe purpose of presenting the new model is to get a high-quality solution of the haplotype assembly problem. To show that MCIH is a potential alternative, we evaluate the model by studying its exact solutions. A special data structure with Markov property suited for a dynamic programming algorithm is considered as follows.li and ri be the beginning and ending positions of the ith SNP fragments fi on the SNP matrix respectively, 1 ≤ i ≤ m. For any two fragments fi and fj, we assumeLet mi and mj, i < j if and only if li < lj. If li = lj and ri < rj, the ith row will also be put before the jth row. From the proof of the computational complexity in The rows of the SNP matrix is reordered according to the beginning positions and the ending positions of SNP fragments. For two rows g with length n and a SNP matrix of the type (2). The outputs of the DP algorithm are a partition of the SNP fragments and a pair of halotypes  generated from the partition. In fact, the DP algorithm is used to reconstruct one haplotype, say h1. Clearly, h2 can be obtained immediately from g and h1.To solve MCIH with this special structure, we give a dynamic programming (DP) algorithm. The input data of the DP algorithm are a genotype x(j) =  is an assignment to the positions lj, ···, rj of h1, and x̄ (j) is the corresponding assignment to h2 at the same positions. If gk ≠ 0, xk =x̄k = gk/2, otherwise xk + x̄k = 0 for k = lj, ···, rj. Let f1) denote the number of conflicts between the SNP fragment fj and h1 with assignment x(j), f2) denote the number of conflicts between the SNP fragment fj and h2 with assignment x̄(j). Define f ) = min{ f1), f2)} which implies the haplotype that the jth fragment belongs to. The main steps of the dynamic programming algorithm are as follows:Suppose that Initialization.j = 1. N) = f ) = min{ f1), f2 )} for all possible x(1).Let Follow the recurrence formula.N) as the minimum associated number of conflicts between the first j fragments and haplotypes  at positions 1, 2, ···, rj, with the assignment x (j).Define bk = gk/2 for gk ≠ 0, otherwise bk ∈{1,−1}, k = lj, ···, lj+1 −1.where h1, h2) and a partition of the SNP fragments.Trace the solution to obtain a pair of haplotypes ) for 1 ≤ j ≤ m are computed by the recurrence formula, we can find N) for all x (m) = . Thus, the solution of the MCIH problem can be obtained by tracing the solution forwardly which leads to a minimal value at each j from the following formula:When all j, f) = min{f1), f2)} determines a partition of the SNP fragments.For all L is the maximum length of SNP fragments. It can be shown that the dynamic programming algorithm solving the special MCIH problem has the complexity O(22Lm). Hence, the algorithm is exponential with the maximum length of SNP fragments. However, when the maximum length of SNP fragments is fixed, this algorithm is linear to m, i.e., the number of fragments. In real applications, the value of L is generally between 3 and 8.Assume that As expected, numerical results . In our problem, a set of SNP fragments are to be divided into two classes such that each class can be assembled as one of the haplotypes which will solve the problem with as few conflicts as possible.As discussed in Introduction, the MCIH problem is actually a classification problem. That is, given a set of SNP fragments, we want to classify it into two fragment subsets such that each subset determines a haplotype to solve the problem with minimum conflicts. It is well known that feed-forward neural network (FNN) is a powerful tool for classification. In general, an FNN maps a set of objects into classes m input neurons represent m SNP fragments. Each input neuron accepts an n-dimensional vector on {1, −1, 0}. Two hidden neurons represent two subsets of fragments corresponding to one pair of haplotypes. The input dimension of the hidden neuron is also n and the outputs of the two neurons are a pair of tentative haplotypes. There is only one neuron in the last layer, which simply conflates the tentative haplotypes and outputs a tentative genotype. Comparing this tentative genotype with the given genotype will provide us the information to adjust the neuron connection weights by the popular back-propagation algorithm  and y2 = , whereThe inputs to the second layer are Two neurons in the hidden layer have the same sigmoidal neuron function:h1 =  and h2 = , whereThe outputs of the second layer neurons are h1 and h2. The neuron function of the third layer is a linear summation, i.e., the output of the third layer is z = , whereThe inputs to the third layer are h1, h2) toThe MCIH problem is to find a pair of haplotypes  of f1, ···, fm and all possible pairs of haplotypes , andfor all partitions , where g =  is the given genotype.for all possible pairs of haplotypes  is defined asThe distance between d is defined by the formula (1) andwhere h1 and h2, i.e.,Classify all SNP fragments into two disjoint sets according to their distances to h1 in the second layer, the network learns to minimize the following error function between h1 and the SNP fragments in F1:For the neuron corresponding to h2, the network learns to minimize the error function between h2 and the SNP fragments in F2:For the neuron corresponding to z and the original genotype g:The objective that the third layer adjusts to is to minimize the difference between the tentative genotype The main steps of the algorithm is as follows:L1, L2, ρ, λ and ɛ. Randomly set the initial values of the weight matrix W(0) with entries wil ∈ , i = 1, ···, m, l = 1, 2. t = 0.Set parameter values h1, h2).Input a SNP matrix. Do computation of (3), (5) and (6) to get a pair of tentative haplotyes  using  according to the formulae (10) and (11).Substep 2.2: Calculate the gradients of the error functions ∇w1R11, ∇w2R12, ∇w1R2, ∇w2R2 according to (17), (18), (19), and (20).Substep 2.3: Update the current weight matrix W (t) bywhereρ is the step length along the negative gradient. L1 and L2 are parameters to be selected.Substep 2.4: If ||W (t + 1) − W (t)|| < ɛ, go to Step 3, otherwise return to Step 1.ĥ1, ĥ2) and a genotype ĝ as the output.Record a pair of haplotypes , the similarity degree between the original haplotypes and the reconstructed haplotypes, to measure performance of an algorithm or a model. Assume that h =  is the original pair of haplotypes, and ĥ =  is the reconstructed pair of haplotypes. We define RR as:In our experiments, we use rij = HD , i = 1, 2, j = 1, 2. We use compatibility rate (CR) to measure the similarity degree between the original genotype g and the reconstructed genotype ĝ:where P =  and a pair of haplotypes , the corresponding conflict number is defined by (7).For a partition ρ = 0.05, ɛ = 0.01 and λ = 0.1  in the algorithm. In (15) and (16), we set L1 = 0.2 and L2 = 0.8.In this paper, we set step length Angiotensin-converting enzyme catalyses the conversion of angiotensin I to the physiologically active peptide angiotensin II, which controls fluid-electrolyte balance and systemic blood pressure. Because it has a key function in the renin-angiotensin system, many association studies have been performed with DCP1 (encode angiotensin-converting enzyme). Literature  completem = 20; other parameters, the hole rate of fragments hr: 0.25, 0.5, 0.75; the error rate of fragments e: 0.05, 0.1, 0.15, 0.2 and 0.25.Among these 11 individuals, there are two identical genotypes. We omit one of them. In addition, we omit the genotypes with no more than one heterozygous site whose haplotypes can be infered immediately. Now each of the 8 pairs of haplotypes is used to generate 15 instances in which SNP fragments are randomly generated according to different parameter settings: the number of SNP fragments The results of MCIH and MEC  averageq31 using 103 SNPs in a European derived population which consists of 129 trios. The haplotypes of 129 children from the trios can be infered from the genotypes of their parents through pedigree information and the nontransmitted chromosomes as an extra 129 (pseudo) haplotype pairs. Markers for which both alleles could not be inferred are marked as missing. Among the resulting set of 258 haplotype pairs, the ones with more than 20% missing alleles are removed, which leaves us 147 haplotype pairs. Among these pairs, 18 genotypes with no more than one heterozygous site are omitted, then 129 pairs of haplotypes are left as the test set.Now we performed simulations using the data from public Daly set . They rem = 30; other parameters, hr: 0.25, 0.5, 0.75; e: 0.05, 0.1, 0.15, 0.2 and 0.25. The results of MCIH and MEC averaged on 129 pairs of haplotypes are illustrated in Each of the 129 pairs of haplotypes is used to generate 15 instances in which SNP fragments are randomly generated according to different parameter settings: The picture again shows that MCIH is quite effective and the designed back-propagation-like algorithm has a very good performance. The genotype compatibility rate returned by the algorithm for most instances is 100% or greater than 98%. Only a few instances with error rate 0.25 and hole rate 0.75 have genotype compatibility rate between 92% and 96%. In addition, the neural network algorithm solves instances with a low error rate and hole rate in several seconds. For a few instances with a high error rate and hole rate, however, it takes the algorithm several minutes to stop.ms [ms has a parameter r as the recombination rate of population haplotypes. Firstly, let r = 0, then 20 haplotypes with 80 SNP sites are generated using ms and form a haplotype set. Then we randomly combine two halotypes in the haplotype set into a pair of individual haplotypes. 15 pairs of haplotypes are obtained by this way. Each of the 15 pairs of haplotypes is used to generate 15 instances according to the parameters as those in the last subsection. All of these instances form a dataset. The results of the two models averaged on 15 pairs of haplotypes are shown in r = 100 and other parameters be the same. The results of two models on this dataset are summarized in We use a well-known program ms  that useFrom Individual haplotyping is an important problem in computational biology. In this paper, we proposed a new computational model for individual haplotyping—MCIH as an improvement of MEC and as an alternative way for biologists to solve the haplotyping problem more efficiently. The model is proved to be NP-hard. To evaluate the new model, we displayed a special SNP matrix structure for which a dynamic programming algorithm can be used to solve MCIH exactly. Comparing the exact solutions of MCIH and MEC, we argue that the proposed MCIH is worth further studying. Due to the computational intractability of the MCIH problem, a feed-forward neural network is designed and a back-propagation-like procedure is formed as an efficient approximate algorithm. Computational results on multiple data sets show that the designed algorithm performs well and MCIH at the cost of additive genotype information has a higher accuracy of haplotype reconstruction than MEC, especially in the case of SNP fragments with a high error rate. Since genotype information can be obtained much easily and economically, the new model is practical as a supporting tool for reconstruction of haplotypes.