BACKGROUND
single nucleotide polymorphisms  have emerged as the genetic marker of choice for mapping disease loci and candidate gene association studies, because of their high density and relatively even distribution in the human genomes  <cit> .
both the international human genome sequencing project and the international hapmap projects have generated large amounts of data on the location, quantity, type, and frequency of genetic variants, in particular of snps, in the human genome  <cit> .
the determination of genome-wide linkage disequilibrium  patterns through the hapmap project has enabled the selection of markers for efficient genome-wide association  studies in human samples <cit> .
recent technological advances in ultra high-throughput genotyping platforms potentially permit the parallel analysis of millions of snps with a significant reduction in genotyping price, making gwa studies a reality  <cit> .
the impact of this approach is readily visible, since over  <dig> publications have been collected in the last three years in the national cancer institute -national human genome research institute 's catalog of published genome-wide association studies  <cit> .
many challenges still exist after this first wave of gwa studies, and one of them is the validation and replication of the positive hits from the gwa screening.
a multistage approach has been suggested to increase gwa efficiency with proper validation steps  <cit> .
a large number of individuals are usually required to be genotyped on a small subset of candidates snps.
many snp genotyping methods have been developed on varieties of platforms in the past decade  <cit> , with several technologies using solid-phase-mediated detection being able to genotype thousands of sites simultaneously, such as the genechip array and the beadarray  <cit> .
these methods have provided ultra multiplex and high-throughput genotyping but are not cost effective for genotyping a large number of samples for a modest number of snps.
homogenous detection methods such as taqman®  <cit>  and molecular beacon -based  <cit>  approaches provide uniplex reactions, and are readily applied to a large number of samples but multiplexing may be difficult.
between these two  methods there is a need for systems allowing medium multiplexing  with high throughput, which can efficiently and cost-effectively generate genotypes for a very large sample set .
therefore, alternative methods that are flexible, fast, accurate and cost-effective are urgently needed.
this is also important for those who work on high throughput genotyping in non-model systems where off-the-shelf assays are not available and a flexible platform is needed.
we have developed a nanofluidic platform to meet the needs of medium multiplex high throughput snp genotyping and demonstrate call rates of >99% and accuracy rates of > <dig> % under uniform assay conditions, on human samples.
RESULTS
chip architecture
the chip used in this study, the  <dig> cs dynamic array, is mounted on a plastic carrier with interface and containment accumulators and  <dig> sample inlets and  <dig> assay inlets, with the dimensions of the inlets and the size of the plate conforming to the standards set by the society for biomolecular sciences .
the chip has the ability to test  <dig> samples with  <dig> snp assays.
these combine in pair wise fashion to produce  <dig> reaction chambers, in a final volume of  <dig>  nl .
a similar chip has been described previously by spurgeon et.al   <cit> .
the integrated fluidic circuit  is a network of fluid lines, nanoflex™ valves and chambers.
the nanoflex™ valves are made of an elastomeric material which deflects under pressure to create a tight seal and are used to regulate the flow of liquids in the ifc.
prior to loading, the chip is primed using the nanoflex™ 4-ifc controller which pressurizes the control lines and closes the interface valves.
individual samples are pipetted into the sample inlets and genotyping taqman® assays are pipetted into the detector inlets.
the chip is then placed back on the nanoflex™ 4-ifc controller for loading and mixing.
during this process, pressure is applied to the fluid in the sample inlets and the fluid is pushed into the sample fluid lines.
at the same time the fluid in the assay inlets is pushed into the assay fluid lines.
mixing of the two fluids is prevented by the closed interface valve.
this chip  utilizes a design known as the carry-over slug  design, providing precise metering of fluid volumes and efficient mixing of the metered volumes.
a first solution  is introduced into a segment of a flow channel in fluidic communication with a reaction chamber.
a second solution  is flowed through the segment so that the first  solution is displaced into the reaction chamber, and a volume of the second  solution enters the chamber.
the chamber is then isolated and reactions within the chamber can be initiated by thermal cycling.
this load-mix process takes approximately  <dig> minutes.
after loading and mixing is complete, the chip is placed on a standalone pcr thermal cycler for pcr.
the volumes involved in performing genotyping on the dynamic array significantly reduces the genotyping reagent consumption and labor cost .
* on the dynamic array using  <dig> μl reaction mixes for  <dig> samples at a time
once pcr is complete, endpoint fluorescent image data is acquired on the biomark™ system for genetic analysis and data is analyzed using the fluidigm snp genotyping analysis software, to obtain genotype calls.
figure 2a shows the raw image from a chip run in both fam  and vic  fluorescent channels.
figure 2b shows a computer generated image of the genotype calls for each of the  <dig> reaction chambers.
each column  represents data from one assay that correlated to the snp genotyping assay loaded from each assayinlet.
each row represents data from one dna sample loaded from each sample inlet.
in brief, the genotyping software calculates the fluorescent signals from both fam and vic channels and plots each sample on a scatter plot using the fam relative intensity  on the x-axis, and vic relative intensity on the y-axis.
we then used a k-means clustering algorithm based on nearest-centroid sorting to automatically classify samples into four genotype groups, or three if the rare homozygous allele is not present in a particular chip run.
figure 2c shows a genotyping scatter plot with genotype calls automatically assigned by the analysis software with  <dig> different colors coded, one for each genotype plus one ntc .
the software is capable of analyzing multiple chip runs simultaneously, and up to  <dig> chip runs were analyzed together in this study .
human snp genotyping
we utilized this nanofluidic system to genotype snps in human genomic dna samples.
the dna screened included  <dig> dna samples extracted from blood from the prostate, lung, colon, and ovarian cancer  screening trial  <cit> , and  <dig> hapmap samples extracted from cell lines.
all samples were double-blinded, and were genotyped on  <dig> dynamic arrays.
chips were thermal cycled using standard conditions , and the end-point fluorescence values were measured on the biomark™ system.
a total of  <dig> μl of each dna sample  was used in a  <dig> μl sample mix to genotype  <dig> snp assays and one non-reagent control.
each chip only needs a total of  <dig> liquid-transfer  steps , which is considerably less pipetting than required to set up  <dig> conventional  <dig> well plates  with the same genotyping output.
we manually set up  <dig> chip runs on each working day with a throughput of about  <dig>  genotypes per day, without using robotic stations.
the endpoint genotype data were analyzed using the fluidigm snp genotyping analysis software .
we achieved a call rate of greater than  <dig> % from the  <dig> genotyped samples on  <dig> snps.
in comparison, we have also used an applied biosystems  <dig> ht with standard microtiter plates and taqman® genotyping methodologies to genotype the  <dig> plco samples on  <dig> snps and the  <dig> hapmap samples on  <dig> snps, and obtained a lower call rate at  <dig> % with almost every snp in comparison to the results obtained on the fluidigm dynamic array .
we have estimated the genotyping concordance between the results from the  <dig> cs chip and the microtiter plates run on the applied biosystems  <dig> ht  system.
among the  <dig> snps genotyped on  <dig> plco samples, all snps gave ≥  <dig> % concordance, except for one with  <dig> %.
among the  <dig> hapmap samples genotyped by both platforms, all snps except three had concordance rates of 99% to 100% .
we next estimated the genotyping accuracy after unblinding the samples.
the genotyping results obtained from the  <dig> hapmap dna samples were compared with the corresponding data in the dbsnp database http://www.ncbi.nlm.nih.gov/projects/snp/.
we achieved a call rate of  <dig> % for all samples across  <dig> genotyped snps  using the biomark™ system.
a total of  <dig> genotypes of the tested snps were found in the dbsnp database for these hapmap samples; our genotyping results matched  <dig> with only  <dig> mismatches, resulting in a  <dig> % concordance.
almost all of the snps except for six gave 100% concordance .
we also determined the reproducibility  of our system.
as mentioned earlier, some of the  <dig> plco samples had been provided in  replicates.
the replicates comprised a total of  <dig> samples, and included  <dig> duplicates ,  <dig> triplicates  and  <dig> quadruplicate  set, resulting in  <dig> pair-wise comparisons.
of the  <dig> pair wise comparisons  <dig> pairs exhibited 100% concordance for  <dig> snps.
of the discordant pairs of samples:  <dig> pairs had a single discordant result;  <dig> pair had two discordant results and  <dig> pair had three discordant results.
of the  <dig> snp assays,  <dig> have no discordance.
of the discordant assays:  <dig> assay exhibited a single discordance;  <dig> assays exhibited two discordances and  <dig> assays exhibited four discordances.
the total reproducibility rate is >  <dig> %.
when analyzing the call rate sample-by-sample, we noticed that  <dig> samples from the adenoma project contributed to over 50% of the total no call rate .
we amplified these  <dig> samples using a specific target amplification  protocol on the  <dig> targeted snps  <cit> .
the amplified samples were diluted five- fold after sta and then genotyped on  <dig> cs dynamic array.
figure  <dig> shows the genotyping results from both genomic and sta dna from these  <dig> samples.
the sta and genomic dna were genotyped on the same chip side-by-side.
the circled data points are from sta samples, which clearly showed much higher signal levels than the genomic dna samples.
with sta, all  <dig> samples except for one, had their genotype call-rate across  <dig> snps improve significantly, with most of them having call rates of up to  <dig> to 100% .
one sample could not be amplified, reflecting the possible presence of some form of pcr inhibitor in the original sample.
the overall call rate for the  <dig> samples improved to  <dig> % with the sta step.
* this sample failed sta, because of possible pcr inhibition.
input dna quantity affects genotyping accuracy
on the  <dig> cs dynamic array, each dna sample is distributed into  <dig> reaction chambers to genotype  <dig> snps.
typically, ~ <dig> μl of dna sample is made up to  <dig> μl with taqman® mastermix and other additives , is loaded into the inlets of each chip, and is distributed into  <dig> reaction chambers, in a final volume of  <dig>  nl per reaction chamber.
when the initial number of dna copies are low, as a result of the reduction in overall volume , the number of actual dna molecules tested becomes limiting.
in the experiments described earlier, each  <dig>  nl reaction chamber contained about  <dig> copies of dna molecules, in contrast to typical microtitre plates, which use about 500- <dig> fold more dna.
to evaluate the effect that dna copy number has on genotyping calls, we tested three genomic dna samples with three different genotypes for a single snp .
different amounts of dna were loaded into dynamic array chips, such that the final dna copy number in reaction chambers ranged from  <dig>  to  <dig> copies of genomic equivalents.
figure  <dig> shows the genotyping clusters obtained with different amounts of input dna.
as can be seen, when  <dig> to  <dig> copies of genomic dna are tested, one gets excellent call rates , with the heterozygotes called correctly in all cases.
as the number of copies tested decrease, the numbers of no-calls and call errors increase.
for example, when the copy number is  <dig> dna molecules per reaction chamber, 22%  of the calls in the heterozygous cases failed to identify the correct genotype.
additionally, as would be predicted, when single molecules are tested, the heterozygotic calls  decrease dramatically, with the number of homozygous calls and no-calls increasing proportionately.
the tightness of the clusters  also decreases as the copy number decreases.
finally, when the dna copy number averages  <dig>  copies per reaction chamber, the no-call rate increases to 40%, 58% and 32% in the case of homozygotes a1a <dig>  heterozygotes a1a <dig> and homozygotes a2a <dig>  respectively.
these no-call rates are significantly higher compared to those when the dna copy number averages  <dig>  copies per chamber .
meanwhile, the error rate of the calls in the heterogeneous case also increases significantly from 32% in the case of  <dig>  copies per chamber to 84% in the case of  <dig>  copies per chamber .
this clearly shows the role that gene copy number and dna concentration have on genotyping call quality and could have a significant impact on the data obtained from previous genotyping studies which have not monitored the amount of amplifiable dna molecules in their sample preparation.
CONCLUSIONS
in the current study we demonstrate the use of a unique nanofluidic genotyping system which is simple to use and permits medium multiplexing  with high throughput, excellent call rates, call accuracy and low cost.
we have demonstrated the use of the  <dig> cs dynamic array with integrated fluidic circuits , by genotyping  <dig> individual human dna samples on  <dig> different snp assays, using nanoliter volumes of reagents.
calls from our platform were validated by selected genotyping of the same samples on the applied biosystems  <dig> ht, while calls from the hapmap samples were compared with results obtained by the hapmap project.
call rates of greater than  <dig> % and call accuracy > <dig> % were achieved from our study, which demonstrates that this is a formidable genotyping platform.
the experimental set up is very simple, with a time-to-result for each sample of about  <dig> hours.
in comparison, similar products by illumina  and sequenom  have reported call rates of only 99% and 90-95% respectively, take days , not hours to obtain results, and have a complicated workflow  <cit> .
while we use taqman assays in our approach, the reduced reaction volume  enables the cost of genotyping to be only  <dig> cents per data point, which compares very favorably with other platforms, which cost more, typically ranging by twice to an order of magnitude.
while a detailed cost comparison is beyond the scope of this manuscript, it has been documented in other publications  <cit> .
thus, our approach has higher call rates, a significantly faster throughput, an easier workflow and lower cost than other medium throughput genotyping systems.
the development of this nanofluidic genotyping system enhances the ability to screen mid-range numbers of snps across hundreds to thousands of samples.

 single nucleotide polymorphisms have emerged as the genetic marker of choice for mapping disease loci and candidate gene association studies, because of their high density and relatively even distribution in the human genomes. both the international human genome sequencing project and the international hapmap projects have generated large amounts of data on the location, quantity, type, and frequency of genetic variants, in particular of snps, in the human genome. the determination of genome-wide linkage disequilibrium patterns through the hapmap project has enabled the selection of markers for efficient genome-wide association studies in human samples. recent technological advances in ultra high-throughput genotyping platforms potentially permit the parallel analysis of millions of snps with a significant reduction in genotyping price, making gwa studies a reality. the impact of this approach is readily visible, since over publications have been collected in the last three years in the national cancer institute -national human genome research institute's catalog of published genome-wide association studies. many challenges still exist after this first wave of gwa studies, and one of them is the validation and replication of the positive hits from the gwa screening. a multistage approach has been suggested to increase gwa efficiency with proper validation steps. a large number of individuals are usually required to be genotyped on a small subset of candidates snps. many snp genotyping methods have been developed on varieties of platforms in the past decade, with several technologies using solid-phase-mediated detection being able to genotype thousands of sites simultaneously, such as the genechip array and the beadarray. these methods have provided ultra multiplex and high-throughput genotyping but are not cost effective for genotyping a large number of samples for a modest number of snps. homogenous detection methods such as taqman® and molecular beacon -based approaches provide uniplex reactions, and are readily applied to a large number of samples but multiplexing may be difficult. between these two methods there is a need for systems allowing medium multiplexing with high throughput, which can efficiently and cost-effectively generate genotypes for a very large sample set. therefore, alternative methods that are flexible, fast, accurate and cost-effective are urgently needed. this is also important for those who work on high throughput genotyping in non-model systems where off-the-shelf assays are not available and a flexible platform is needed. we have developed a nanofluidic platform to meet the needs of medium multiplex high throughput snp genotyping and demonstrate call rates of >99% and accuracy rates of > % under uniform assay conditions, on human samples.  chip architecture the chip used in this study, the cs dynamic array, is mounted on a plastic carrier with interface and containment accumulators and sample inlets and assay inlets, with the dimensions of the inlets and the size of the plate conforming to the standards set by the society for biomolecular sciences. the chip has the ability to test samples with snp assays. these combine in pair wise fashion to produce reaction chambers, in a final volume of nl. a similar chip has been described previously by spurgeon et.al. the integrated fluidic circuit is a network of fluid lines, nanoflex™ valves and chambers. the nanoflex™ valves are made of an elastomeric material which deflects under pressure to create a tight seal and are used to regulate the flow of liquids in the ifc. prior to loading, the chip is primed using the nanoflex™ 4-ifc controller which pressurizes the control lines and closes the interface valves. individual samples are pipetted into the sample inlets and genotyping taqman® assays are pipetted into the detector inlets. the chip is then placed back on the nanoflex™ 4-ifc controller for loading and mixing. during this process, pressure is applied to the fluid in the sample inlets and the fluid is pushed into the sample fluid lines. at the same time the fluid in the assay inlets is pushed into the assay fluid lines. mixing of the two fluids is prevented by the closed interface valve. this chip utilizes a design known as the carry-over slug design, providing precise metering of fluid volumes and efficient mixing of the metered volumes. a first solution is introduced into a segment of a flow channel in fluidic communication with a reaction chamber. a second solution is flowed through the segment so that the first solution is displaced into the reaction chamber, and a volume of the second solution enters the chamber. the chamber is then isolated and reactions within the chamber can be initiated by thermal cycling. this load-mix process takes approximately minutes. after loading and mixing is complete, the chip is placed on a standalone pcr thermal cycler for pcr. the volumes involved in performing genotyping on the dynamic array significantly reduces the genotyping reagent consumption and labor cost. * on the dynamic array using μl reaction mixes for samples at a time once pcr is complete, endpoint fluorescent image data is acquired on the biomark™ system for genetic analysis and data is analyzed using the fluidigm snp genotyping analysis software, to obtain genotype calls. figure 2a shows the raw image from a chip run in both fam and vic fluorescent channels. figure 2b shows a computer generated image of the genotype calls for each of the reaction chambers. each column represents data from one assay that correlated to the snp genotyping assay loaded from each assayinlet. each row represents data from one dna sample loaded from each sample inlet. in brief, the genotyping software calculates the fluorescent signals from both fam and vic channels and plots each sample on a scatter plot using the fam relative intensity on the x-axis, and vic relative intensity on the y-axis. we then used a k-means clustering algorithm based on nearest-centroid sorting to automatically classify samples into four genotype groups, or three if the rare homozygous allele is not present in a particular chip run. figure 2c shows a genotyping scatter plot with genotype calls automatically assigned by the analysis software with different colors coded, one for each genotype plus one ntc. the software is capable of analyzing multiple chip runs simultaneously, and up to chip runs were analyzed together in this study. human snp genotyping we utilized this nanofluidic system to genotype snps in human genomic dna samples. the dna screened included dna samples extracted from blood from the prostate, lung, colon, and ovarian cancer screening trial, and hapmap samples extracted from cell lines. all samples were double-blinded, and were genotyped on dynamic arrays. chips were thermal cycled using standard conditions, and the end-point fluorescence values were measured on the biomark™ system. a total of μl of each dna sample was used in a μl sample mix to genotype snp assays and one non-reagent control. each chip only needs a total of liquid-transfer steps, which is considerably less pipetting than required to set up conventional well plates with the same genotyping output. we manually set up chip runs on each working day with a throughput of about genotypes per day, without using robotic stations. the endpoint genotype data were analyzed using the fluidigm snp genotyping analysis software. we achieved a call rate of greater than % from the genotyped samples on snps. in comparison, we have also used an applied biosystems ht with standard microtiter plates and taqman® genotyping methodologies to genotype the plco samples on snps and the hapmap samples on snps, and obtained a lower call rate at % with almost every snp in comparison to the results obtained on the fluidigm dynamic array. we have estimated the genotyping concordance between the results from the cs chip and the microtiter plates run on the applied biosystems ht system. among the snps genotyped on plco samples, all snps gave ≥ % concordance, except for one with %. among the hapmap samples genotyped by both platforms, all snps except three had concordance rates of 99% to 100%. we next estimated the genotyping accuracy after unblinding the samples. the genotyping results obtained from the hapmap dna samples were compared with the corresponding data in the dbsnp database http://www.ncbi.nlm.nih.gov/projects/snp/. we achieved a call rate of % for all samples across genotyped snps using the biomark™ system. a total of genotypes of the tested snps were found in the dbsnp database for these hapmap samples; our genotyping results matched with only mismatches, resulting in a % concordance. almost all of the snps except for six gave 100% concordance. we also determined the reproducibility of our system. as mentioned earlier, some of the plco samples had been provided in replicates. the replicates comprised a total of samples, and included duplicates, triplicates and quadruplicate set, resulting in pair-wise comparisons. of the pair wise comparisons pairs exhibited 100% concordance for snps. of the discordant pairs of samples: pairs had a single discordant result; pair had two discordant results and pair had three discordant results. of the snp assays, have no discordance. of the discordant assays: assay exhibited a single discordance; assays exhibited two discordances and assays exhibited four discordances. the total reproducibility rate is > %. when analyzing the call rate sample-by-sample, we noticed that samples from the adenoma project contributed to over 50% of the total no call rate. we amplified these samples using a specific target amplification protocol on the targeted snps. the amplified samples were diluted five- fold after sta and then genotyped on cs dynamic array. figure shows the genotyping results from both genomic and sta dna from these samples. the sta and genomic dna were genotyped on the same chip side-by-side. the circled data points are from sta samples, which clearly showed much higher signal levels than the genomic dna samples. with sta, all samples except for one, had their genotype call-rate across snps improve significantly, with most of them having call rates of up to to 100%. one sample could not be amplified, reflecting the possible presence of some form of pcr inhibitor in the original sample. the overall call rate for the samples improved to % with the sta step. * this sample failed sta, because of possible pcr inhibition. input dna quantity affects genotyping accuracy on the cs dynamic array, each dna sample is distributed into reaction chambers to genotype snps. typically, ~ μl of dna sample is made up to μl with taqman® mastermix and other additives, is loaded into the inlets of each chip, and is distributed into reaction chambers, in a final volume of nl per reaction chamber. when the initial number of dna copies are low, as a result of the reduction in overall volume, the number of actual dna molecules tested becomes limiting. in the experiments described earlier, each nl reaction chamber contained about copies of dna molecules, in contrast to typical microtitre plates, which use about 500- fold more dna. to evaluate the effect that dna copy number has on genotyping calls, we tested three genomic dna samples with three different genotypes for a single snp. different amounts of dna were loaded into dynamic array chips, such that the final dna copy number in reaction chambers ranged from to copies of genomic equivalents. figure shows the genotyping clusters obtained with different amounts of input dna. as can be seen, when to copies of genomic dna are tested, one gets excellent call rates, with the heterozygotes called correctly in all cases. as the number of copies tested decrease, the numbers of no-calls and call errors increase. for example, when the copy number is dna molecules per reaction chamber, 22% of the calls in the heterozygous cases failed to identify the correct genotype. additionally, as would be predicted, when single molecules are tested, the heterozygotic calls decrease dramatically, with the number of homozygous calls and no-calls increasing proportionately. the tightness of the clusters also decreases as the copy number decreases. finally, when the dna copy number averages copies per reaction chamber, the no-call rate increases to 40%, 58% and 32% in the case of homozygotes a1a heterozygotes a1a and homozygotes a2a respectively. these no-call rates are significantly higher compared to those when the dna copy number averages copies per chamber. meanwhile, the error rate of the calls in the heterogeneous case also increases significantly from 32% in the case of copies per chamber to 84% in the case of copies per chamber. this clearly shows the role that gene copy number and dna concentration have on genotyping call quality and could have a significant impact on the data obtained from previous genotyping studies which have not monitored the amount of amplifiable dna molecules in their sample preparation.  in the current study we demonstrate the use of a unique nanofluidic genotyping system which is simple to use and permits medium multiplexing with high throughput, excellent call rates, call accuracy and low cost. we have demonstrated the use of the cs dynamic array with integrated fluidic circuits, by genotyping individual human dna samples on different snp assays, using nanoliter volumes of reagents. calls from our platform were validated by selected genotyping of the same samples on the applied biosystems ht, while calls from the hapmap samples were compared with results obtained by the hapmap project. call rates of greater than % and call accuracy > % were achieved from our study, which demonstrates that this is a formidable genotyping platform. the experimental set up is very simple, with a time-to-result for each sample of about hours. in comparison, similar products by illumina and sequenom have reported call rates of only 99% and 90-95% respectively, take days, not hours to obtain results, and have a complicated workflow. while we use taqman assays in our approach, the reduced reaction volume enables the cost of genotyping to be only cents per data point, which compares very favorably with other platforms, which cost more, typically ranging by twice to an order of magnitude. while a detailed cost comparison is beyond the scope of this manuscript, it has been documented in other publications. thus, our approach has higher call rates, a significantly faster throughput, an easier workflow and lower cost than other medium throughput genotyping systems. the development of this nanofluidic genotyping system enhances the ability to screen mid-range numbers of snps across hundreds to thousands of samples. 
our results demonstrate that the dynamic array is an excellent genotyping system for medium-throughput multiplexing, which is simple to use and combines rapid throughput with excellent call rates, high concordance and low cost. there is a need for systems allowing medium multiplexing with high throughput, which can efficiently and cost-effectively generate genotypes for a very large sample set. we demonstrate the use of a nanofluidic integrated fluidic circuit - based genotyping system for medium-throughput multiplexing known as the dynamic array, by genotyping individual human dna samples on different snp assays, using nanoliter volumes of reagents. the experimental set up is very simple, with a time-to-result for each sample of about hours. single nucleotide polymorphisms have emerged as the genetic marker of choice for mapping disease loci and candidate gene association studies, because of their high density and relatively even distribution in the human genomes. this is also important for those who work on high throughput genotyping in non-model systems where off-the-shelf assays are not available and a flexible platform is needed.
 single nucleotide polymorphisms have emerged as the genetic marker of choice for mapping disease loci and candidate gene association studies, because of their high density and relatively even distribution in the human genomes. there is a need for systems allowing medium multiplexing with high throughput, which can efficiently and cost-effectively generate genotypes for a very large sample set. methods that are flexible, fast, accurate and cost-effective are urgently needed. this is also important for those who work on high throughput genotyping in non-model systems where off-the-shelf assays are not available and a flexible platform is needed.  we demonstrate the use of a nanofluidic integrated fluidic circuit - based genotyping system for medium-throughput multiplexing known as the dynamic array, by genotyping individual human dna samples on different snp assays, using nanoliter volumes of reagents. call rates of greater than % and call accuracies of greater than % were achieved from our study, which demonstrates that this is a formidable genotyping platform. the experimental set up is very simple, with a time-to-result for each sample of about hours.  our results demonstrate that the dynamic array is an excellent genotyping system for medium-throughput multiplexing, which is simple to use and combines rapid throughput with excellent call rates, high concordance and low cost. the exceptional call rates and call accuracy obtained may be of particular interest to those working on validation and replication of genome- wide- association studies. 