great improvements in recent smartphone technologies , specially in terms of sensing capabilities , has resulted in the emergence of a new sensing paradigm , known as _ participatory sensing _ .the main idea behind participatory sensing applications is involvement of ordinary citizens who collect sensor readings using their mobile phones .this revolutionary paradigm has been practically used to obtain information ranging from personal health and prices of customer goods to environmental monitoring such as road conditions and noise pollution .  for a participatory sensing campaign to be a success ,a number of challenges exist that should be addressed properly .the first challenge is the recruitment of sufficient participants , when there may be no explicit incentive .the second challenge is the suitability of participants to attend in the campaigns which require specific expertise or knowledge ( such as taking photos of rare plant species ) .the third challenge is the trustworthiness of the contributions due to the open nature of participatory sensing .  to address these challenges in a comprehensive manner ,one proposed idea is to leverage online social networks as the underlying infrastructure for participatory sensing applications .the resulting paradigm , known as _ social participatory sensing _ enables the usage of friendship relations for the identification and recruitment of participants .  in our previous work , we addressed the challenge of assessing contribution trustworthiness by proposing a trust framework for social participatory sensing systems . in this framework ,one - hop friends were selected as participants , and the trustworthiness of each contribution was calculated by considering the quality of contribution and trustworthiness of participant as two important trust aspects .these two aspects were then combined using fuzzy logic to reach to a trustworthiness score for each contribution , which was then used as a metric to select reliable contributions .  leveraging immediate friends for participatorysensing data collection is beneficial since friends usually tend to be useful to their friends .however , in the absence of adequate friendship relations , the challenge of sufficient participants still exists .consider a situation in which , a requester has few friends and not all of them attend in the sensing campaign .there might also be a case where a person has several friends but only a handful may contribute .in such cases , the validity of the derived information becomes a big concern due to the lack of sufficient contributions .in other words , with one - hop recruitment method , participant selection faces a potential constraint due to its limitation to the circle of immediate friends .this constraint will result in limited access to additional sources of data .an extension to this recruitment method is to go deeper through the social graph and recruit friends of friends ( fofs ) to attend in sensing campaigns .such _ multi - hop _ recruitment offers added values such as access to a large group of participants as valuable resources via friendship network of friends .it also increases the probability of access to well - suited participants with distinguished capabilities who are able to offer new perspectives and provide trustworthy contributions .  developing a multi - hop recruitment strategy, however , opens up multiple questions .the first question being _ who should be recruited ? _ a trivial approach is to broadcast the task to all friends and fofs . in this case , all friends and fofs receive the task and are able to attend in the sensing campaign .however , in case of multi - hop recruitment , task flooding is not rational due to considerable overhead in terms of time , effort and network traffic .an alternative approach is to add a pre - selection process to the recruitment function , which is responsible for traversing through the requester s social graph ( up to a specified level ) and identifying suitable participants , i.e. , those who can satisfy the task s requirements in an acceptable level .an indicator for this suitability is participant s performance in a past series of sensing campaigns .participant s trustable contributions for several campaigns with specific knowledge or expertise requirements , demonstrates his suitability for future recruitment in similar tasks .the same stands for participants who always act in a timely manner , which makes them suitable candidates for attending in real - time contributions .such valuable information should be extracted and used for recruitment of participants in future campaigns .another question in multi - hop recruitment is _ how participants should be recruited ? _ when suitable participants are selected , tasks should be sent from the requester to participants traversing social links . for a selected participant, there may be more than one route to reach . in this case, it is desirable to transfer the task from the most trustable path , to assure that the task s content and specifications has not been changed .since recruitment process is performed for each sensing campaign , one may wonder whether there is _ any suggested participant to be recruited in future campaigns ?_ in fact , sometimes a participant demonstrates an acceptable behaviour in terms of providing highly trustable contributions in a series of related campaigns .for example , he provides satisfiable contributions with high related expertise for a series of tasks which require taking photos of rare plant species .in such a case , it will be beneficial to add this participant to a suggestion list to be recruited for further campaigns .  in this paper , in order to fully address the above mentioned questions ,we propose a trust - based recruitment framework for social participatory sensing systems .our recruitment framework has four major components : the first component , called _ participant selection _ , leverages multi - hop friendship relations in order to involve friends of friends in the sensing campaign and identify suitable candidates .the second component , called _ route selection _ , identifies the most trustable routes to selected participants .once suitable participants are recruited , the third component , the _ trust server _ , evaluates the contributions from selected participants and assigns a trust score that reflects the quality of the contributions .the forth component , called _ suggestion component _ , provides the requester with a connection suggestion list containing a set of participants who have shown trustworthy behaviour in previous campaigns and are proper candidates for further recruitment or friendship establishment .the rest of the paper is organised as follows .related work is discussed in section [ rel ] .we present the details of our framework in section [ pro ] .simulation results are discussed in section [ sim ] .finally , section [ con ] concludes the paper .to the best of our knowledge , the issue of participant recruitment in social participatory sensing hasnt been addressed in prior work . as such , we discuss about related research focussing on recruitment issues in participatory sensing and crowdsourcing .a typical recruitment framework receives the task requirements and provides the requester with a set of well - suited participants .task requirements may involve a variety of factors including the task deadline , location of data collection , mobile device sensing capability and required expertise .the process of identifying suitable participants is usually performed considering parameters such as availability , reputation and expertise , as described below .   for most participatory sensing applications ,availability of participants in terms of geographic and temporal coverage of the sensing area is important . for example , in a noise map sensing campaign , it is desirable to recruit participants who regularly pass through the sensing region and cover as much of the area as possible . proposed a recruitment framework for participatory sensing systems which aims at identifying suitable participants based on parameters such as geographical and temporal availability of participant .participants have previously collected location traces for a period of time that represent their typical behaviour .the location traces are used by the qualifier component to select participants who have the task minimum location requirements .once participants are selected , the assessment component identifies which subset of participants maximizes coverage over task specific area .these participants are then recruited and begin producing sensor data . during the campaign ,the progress review component periodically monitors the coverage and availability of participants to see whether they remain consistent with task requirements . also proposed a distributed recruitment and data collection framework for opportunistic sensing .the recruitment component exploits the suitability of user behaviours and based on the mobility history information , it recruits only the nodes that are likely to be in the sensing area when the sensing activity is taking place . as a distributed recruitment framework ,a set of recruiting nodes visit the sensor area before the campaign is launched and then disseminate recruitment messages . in order to transfercollected sensor data to the requester , a collection of nodes called data sinks are used and participating nodes opportunistically exploit ad hoc encounters to reach data sinks that are temporarily deployed in the sensed area .tracking participation in terms of performance has been employed widely by internet businesses for commissioned work , such as amazon mechanical turk and guru.com , which keep detailed statistics tracking the performance of requesters and workers . moreover , auctioning systems such as e - bay have transaction ratings to help evaluate whether a particular participant is trustworthy .  narrowing it down to participatory sensing ,authors of have presented a group of participatory sensing reputation metrics categorized into two groups : cross - campaign metrics such as number of previous campaigns taken and the success of participant in previous campaigns , and campaign - specific metrics such as timeliness , relevancy and the quality of sensor data . however , in , they have limited reputation to considering participants willingness ( given the opportunity , is data collected ) and diligence in collecting samples ( timeliness , relevance and quality of data ) . proposes a reputation - based framework which makes use of beta reputation to assign a reputation score to each sensor node in a wireless sensor network .beta reputation has simple updating rules as well as facilitates easy integration of ageing .however , it is less aggressive in penalizing users with poor quality contributions .a reputation framework for participatory sensing was proposed in .a watchdog module computes a cooperative rating for each device according to its short - term behaviour which acts as input to the reputation module which utilizes gompertz function to build a long - term reputation score .expert based recruitment consists of identifying persons with relevant expertise or experience for a given topic .expert finding have been excessively studied in social networks . developed a bayesian hierarchical model for expert finding that accounts for both social relationships and content .the model assumes that social links are determined by expertise similarity between candidates . proposed a propagation based approach for finding expert in a social network .the approach consists of two steps . in the first step ,they make use of person local information to estimate an initial expert score for each person and select the top ranked persons as candidates .the selected persons are used to construct a sub - graph . in the second step ,one s expert score is propagated to the persons with whom he has relationships .our recruitment framework is different from all above mentioned methods , since it considers social accountability of participants as an important factor in their reputation and selects well - suited participants who are highly reputable and satisfy the expertise and locality requirements of the tasks .in this section , we explain the proposed framework for trust - based recruitment in social participatory sensing systems .an overview of the architecture is presented in section [ arc ] followed by a detailed discussion of each component in section [ comp ] .[ fig : frm ] illustrates the architecture of the proposed framework .the social network serves as the underlying publish - subscribe substrate for recruiting friends and fofs as participants .in fact , the basic participatory sensing procedures ( i.e. , task distribution and uploading contributions ) are performed by utilizing the social network communication primitives .we abstract an online social network ( e.g. , facebook ) as a weighted directed graph , in which , social network members serve as graph nodes and friendship relations denote the edges of graph , with weights equal to the trust rating between users . a person wishing to start a participatory sensing campaign acts as a requester and defines the task , which includes the specification of task s main requirements such as needed expertise or location . then , participant selection component utilizes the social graph to investigate requester s friends and fofs as potential participants to see whether there is a match between the task requirements and participants attributes .the result will be a set of participants who are suitable to attend in the sensing campaign .next , route selection component traverses the social graph to find the most trustable path from requester to each of selected participants .the task is then routed from the specified paths and delivered to the participants .the contributions received in response to a campaign are transferred ( e.g. , by using facebook graph api ) to a third party trust server which incorporates the fuzzy inference system and arrives at an objective trust rating for each contribution .based on contributions trust rating , mutual trusts along the route between participants and requester are updated .  in certain intervals ( which is after attending in a certain number of campaigns ) , the suggestion component builds a custom contact group for each requester , which contains a list of participants who have shown a satisfiable performance in multiple campaigns . the list is further used for recruitment or friendship establishment .more details about each component is presented in section [ comp ] .this section provides a detailed explanation of framework components .in particular , we go through the new components introduced in this paper , i.e. , participant selection , route selection and suggestion component . we also present a short explanation of what we have done in our previous works .+   the participant selection component is responsible for detecting suitable participants .as said before , we assume that participants are social network members with public profile information as well as social links to their friends . in our previous work, we assumed that for each participant , two types of trust information are stored in the trust database : one is related to personal factors such as participant _ expertise _ , his _ locality _ score to different geographical areas and his _ timeliness _ in responding to campaigns ; the other one is related to social factors such as _ friendship duration _ between participant and each of his friends , and the _ timegap _ between their successive interactions .when a task is defined , a set of requirements including the sensing region , deadline and required expertise is specified . in our previous work, we only assumed one - hop recruitment .so , the task was easily broadcast to all one - hop friends .here , with the assumption of multi - hop recruitment , a pre - process step is necessary in order to find suitable participants who are able to satisfy the task s requirements .so , participant selection component performs a search in the related information of requester s friends and fofs ( up to a specified level ) to detect suitable participants .suitability is a general concept and depends on the requester s point of view . here , we define suitability as an acceptable match between participant s trust factors and the task requirements . in particular , is regarded as a suitable participant if he has an acceptable ( above a predefined threshold ) locality score in the task region and acceptable expertise score in regards to the task s required expertise .these scores are evaluated and stored in the trust server ( details in section [ server ] ) .the aforementioned thresholds are set by the requester while defining the task specifications and requirements .the output of this component is a list of suitable participants who are eligible to be recruited in the campaign .+     once suitable participants are selected , route selection component finds the most trustable routes from the requester to these participants .this process is quite similar to the problem of finding the best routes between two nodes in a directed graph .consider the graph depicted in fig .[ fig : graph ] , in which , is the requester and , , , and are participants ; among which , and have been selected as suitable participants . as mentioned before ,the edges of social graph are labeled with weights equal to the trust score between the nodes .for example , the weight of an edge from to is 0.7 , showing the trust score of upon .if there are intermediate nodes in the route from requester to a participant , trust score of the route is a combination of trust scores of each pair nodes along the route .we leverage multiplication as the combination function since it has been shown in to be an effectiveness strategy for trust propagation . in this case , the trust score of the route from to ( through ) will be 0.7 * 0.65=0.455 .route selection component begins its search from the requester and tries to find the best route to each selected participant .we assume that the best route to immediate friends is the direct edge . for other participants ,route selection component performs a depth first search through the social graph to reach to each selected participant .the search is finished when all routes from the requester to every suitable participant is defined . if more than one route is found between requester and a participant , the route with greatest trust score is selected as the most trustable route .for example , as shown in fig .[ fig : graph ] , there are 5 routes to reach : +  + trust()= + trust()= + trust()= + trust()= + trust()= + the most trustable route between and is .+   when trustable routes to participants are defined , tasks are disseminated from requester to selected participants through selected routes .the trust server is responsible for maintaining and evaluating a comprehensive trust rating for each contribution , updating mutual trusts and calculating a reputation score for each participant .  in our previous work , we assumed that there are two trust aspects that need to be considered : ( 1 ) quality of contribution ( qoc ) and ( 2 ) trust of participant ( top ) .the server maintains a trust database , which contains the network topology as well as required information about participants and the history of their past contributions .when a contribution is received to the trust server , the effective parameters that contribute to the two aforementioned components are evaluated by the evaluator and then combined to arrive at a single quantitative value in the range of [ 0 , 1 ] for each . to evaluate qoc, we assumed of leveraging state - of - the - art methods such as image processing techniques ( for image - based campaigns ) and outlier detection techniques ( for sound - based campaigns ) .as for top , personal parameters such as participant s _ expertise _ , _ timeliness _ and _ locality _ as well as social parameters such as _ friendship duration _ and _ interaction timegap _ between requester and participant are evaluated and combined via a weighted sum aggregation function ( the detailed description of each parameter along with its evaluation method has been thoroughly discussed in ) .the two measures qoc and top serve as inputs for the fuzzy inference system , which computes the trustworthiness of contribution ( toc ) in the range of [ 0 , 1 ] .in fact , the fuzzy system is aimed at adjusting toc according to different values of qoc and top .it should be noted that top denotes the trustworthiness of participant from the requester s point of view . in case of one - hop recruitment ,the requester is participant s immediate predecessor , which may not be always true in multi - hop recruitment .so , in order to correctly compute top in multi - hop recruitment , it should be combined with trustworthiness of the route from requester to the participant , before being fed to the fuzzy system . in other words , will be considered as top in which , is the trustworthiness score of the route from the requester to the participant .once toc is calculated , contributions with toc lower than a predefined threshold are revoked from further calculations .for certain campaigns , depending on the nature of task , the requester may desire to add a subjective evaluation in order to indicate how much the contribution is compatible with his needs and expectations .this subjective rating is referred to as requester evaluation ( re ) and is in the range of [ 0 , 1 ] .  since intermediate friends contribute in the task dissemination process , it is reasonable to update their trust scores at the end of each campaign .consider a situation in which , there are intermediate friends , , , ... , between and .similar to our previous work , we adopt a reward / penalty policy for this update .if the computed toc value for participant is greater than a predefined threshold1 , and all intermediate friends are rewarded , and the amount of is added to each of , in which , is the reputation score of the requester .similarly , if toc value of s contribution is less than a predefined threshold2 , he is penalized , and the amount of is reduced from . trust update process can be summarized in eq .[ eq : tr ] , in which , . in our simulations in section [ sim] , we set and .    at regular intervals , a reputation score in the range of [ 0 , 1 ] is also calculated for each participant , which is a combination of the trust ratings that requesters have assigned to him .this reputation score is further used as a weight for participant s evaluations , ratings or reviews .more details about the process of trust update and reputation score calculation can be found in .+   if demonstrates outstanding performance in multiple campaigns originated by , it would be beneficial if a direct relation is established between them , since : _i ) _ the time required for selecting as a suitable participant in further campaigns is reduced . _ii ) _ less time and effort is consumed for task dissemination , since there is now only one - hop distance to . _iii ) _ an easier access to s friendship network is now available .  to provide with a suggestion list , the following process is performed by suggestion component : + for each participant who is not an immediate friend of , a field called _ implicit trust _ is kept .this field is initially set to zero and is updated whenever contributes to a task originated from .the implicit trust update process is the same as trust update performed in trust server ; i.e. , it is increased by a constant amount , if provides a contribution with toc greater than threshold1 , and decreased by the same amount if toc is less than threshold2 .  at certain intervals ,implicit trust values are investigated to see whether is eligible to be suggested for recruitment or friendship establishment . if above a threshold , has such eligibility . in our simulation, we set this threshold to be 0.5 .  in case of plenty of eligible participants, suggestion component chooses the best candidates among them .the best candidates are those participants who act as intermediate node in larger number of routes .adding such candidates as immediate friends will cause a considerable reduction in path lengths to other participants .  the requester is then provided with a suggestion list which consists of participants ids , their implicit trusts , and the intermediate nodes along the way from the requester to participant .requester utilizes this list for further recruitment or new friendship establishment with initial trust value equal to implicit trust .this section presents simulation - based evaluation of the proposed recruitment system .the simulation setup is outlined in section [ setup ] and the results are in section [ res ] .   to undertake the preliminary evaluations outlined herein , we chose to conduct simulations , since real experiments in social participatory sensing are difficult to organise .simulations afford a controlled environment where we can carefully vary certain parameters and observe the impact on the system performance .we developed a custom java simulator for this purpose .the data set that we use for our experiment is the real web of trust of advogato.org .advogato.org is a web - based community of open source software developers in which , site members rate each other in terms of their trustworthiness .trust values are one of the three choices master , journeyer and apprentice , with master being the highest level in that order .the result of these ratings among members is a rich web of trust , which comprises of 14,019 users and 47 , 347 trust ratings .the distribution of trust values in the advogato web of trust is as follows : master : 17,306 , journeyer : 21,353 , and apprentice : 8688 .the instance of the advogato web of trust referenced in this paper was retrieved on october 13 , 2007 . in order to conform the advogato web of trust to our framework, we map the textual ratings in the range of [ 0 , 1 ] as master = 0.8 , journeyer = 0.6 , and apprentice = 0.4 . the advogato web of trust may be viewed as a directed weighted graph , with users as the vertices and trust ratings as the directed weighted edges of the graph .so , it is in perfect match with our assumptions related to participants and their trust relations in social participatory sensing .whenever a task is launched , one of the advogato users is selected to be the requester .participant selection component traverses the advogato graph beginning from the requester until l level to find suitable participants ( l has been set to be 3 ) .next , route selection component finds the most trustable paths to these participants .tasks are then disseminated through selected routes and trust server calculates toc for each receiving contribution .trust ratings along the routes are then updated ( details have been presented in section [ comp ] ) .we run the simulation for 20 intervals , each consisting of launching 30 tasks . at the end of each interval ,a list containing suggested friends is provided with each requester , who are recruited in further tasks .the list length is set to be 50 .we compare the performance of our framework against one - hop recruitment system described in our previous work , which broadcasts the tasks to all immediate friends .we also consider a multi - hop recruitment system in which , no further friends are added .specifically , we compare the following : ( 1 ) one - hop recruitment , which disseminates the task to all one - hop friends .( 2 ) multi - hop recruitment without suggestion , in which , suitable participants are selected through multi - hop friendship relations and engaged in sensing campaign .no further friendship establishment is done .( 3 ) multi - hop with fs : our proposed framework , which is the same as the second one with suggestion component , which provides the requester with a set of suggested friends to be recruited in subsequent campaigns .  as mentioned in section .[ comp ] , a toc rating is calculated for each contribution in trust server and those with toc lower than a predefined threshold are revoked from further calculations .the tocs for the non - revoked contributions are then combined to form an overall trust for that campaign .in other words , in which , _n _ is the number of non - revoked contributions .the revocation threshold is set to 0.5 .we consider the overall trust as the evaluation metric .greater overall trust demonstrates better ability to achieve highly trustable contributions and revoke untrusted ones .overall trust has a value in the range of [ 0 , 1 ] .moreover , as we explained in section [ comp ] , the route selection component searches for the most trustable routes , which is the route with greatest trust score .this route , however , may not be the route with shortest length .accessing trustable routes with shorter lengths is desirable since longer routes increase the task response time .thus , in order to evaluate the performance of our recruitment framework in terms of finding the best routes , we compute mean route trust ( mrt ) .mrt is defined as sum of trust ratings of all routes from the requester to selected participants divided by sum of route lengths , where route length is denoted by the number of hops .higher mrt demonstrates the better ability to find more reliable routes with shorter lengths .mrt is calculated after each campaign for all selected routes to all selected participants .mrt is a value in the range of [ 0 , 1 ] .figure [ fig : nop ] demonstrates the average number of participants who have been selected and attended in the sensing campaigns inside each interval for both multi - hop and multi - hop with fs recruitment methods . as one - hop recruitment method has few participants in comparison with multi - hop , we omit its related data from this figure to better show the difference between two multi - hop recruitment methods .note that interval 0 is in fact the first interval in which , no new friend has been suggested / added yet and hence , both multi - hop methods recruit equal number of participants .as this figure shows , our proposed recruitment framework is able to recruit participants with an average number twice as multi hop recruitment method , which demonstrates a better performance in terms of finding and recruiting more suitable participants .note that participant selection process in both multi - hop and multi - hop with fs recruitment methods is limited to l levels .but since our framework adds a set of suitable participants as immediate friends , an easier access to friends of these newly added friends ( who may be located beyond the l levels ) becomes available , which results in an increase in potential suitable participants .this demonstrates the effectiveness of our suggestion component in addressing the first challenge of participatory sensing systems described in section [ intro ] , which is recruiting adequate participants .figure [ fig : mrt ] depicts the evolution of average mrt for 20 intervals .as this figure shows , the average mrt of our proposed recruitment framework is higher than the traditional multi - hop method .this means that our framework demonstrates better performance in finding trustable routes which , at the same time , have shorter lengths .one may argue that average mrt for one - hop recruitment method should be higher given that the route length in such method is less than route lengths in multi - hop recruitment methods .we would say that although route lengths in on - hop method are shorter , the sum of trust ratings for selected routes in multi - hop method is greater than one - hop method .the reason is that , as described in section [ server ] , _ all _ the intermediate nodes along the route from the requester to participant are encountered an increase in their mutual trust ratings ( in case of participant s satisfiable behaviour ) .this means that larger number of edges in multi - hop recruitment methods encounter such increase , as compared to one - hop method , which leads to higher mrt for multi - hop recruitment methods .finally , figure [ fig : overall ] demonstrates the evolution of average overall trust obtained from contributions with multi - hop recruitment methods .as this figure depicts , our proposed recruitment framework obtains higher average overall trust in comparison with other method .this is because our framework is able to identify more trustable routes to participants , which results in higher tocs and hence , higher overall trusts .this clearly shows the success of our framework in recruiting suitable participant who produce trustworthy contributions .to summarise , simulation results demonstrate a better performance for our multi - hop with fs framework in terms of achieving higher overall trust ( 0.4 greater than multi - hop method ) with higher mrt .in this paper , we proposed a trust based recruitment framework for social participatory sensing system .our system leverages multi - hop friendship relations to identify well - suited participants .the system then provides trustable routes to selected participants by performing a depth first search in social graph . at specific intervals ,requesters are supported with a set of suggested participants who can be recruited in further campaigns .simulations demonstrated that our scheme increases the overall trust as compared to other methods , and provides trustable routes with shorter lengths for participant recruitment .s. reddy , a. parker , j. hyman , j. burke , d. estrin , and m. hansen , _ image browsing , processing , and clustering for participatory sensing : lessons from a dietsense prototype _ , in acm emnets07 , pp .13 - 17 , 2007 .haleh amintoosi , salil s. kanhere , _ providing trustworthy contributions via a reputation framework in social participatory sensing systems _ , technical report unsw - cse - tr-201304 , university of new south wales , january 2013 .

Abstract: the idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign . however , the selection of suitable participants who are trustable and provide high quality contributions is challenging . in this paper , we propose a recruitment framework for social participatory sensing . our framework leverages multi - hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends , and finds the most trustable paths to them . the framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them , which can be further used for recruitment or friendship establishment . simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well - suited participants and providing contributions with high overall trust , in comparison with one - hop recruitment architecture .   social participatory sensing ; trust ; online social networks ; participatory sensing


the potential of genome - wide association studies ( gwas ) to enable an unbiased search for disease loci across the entire human genome provides us with an unprecedented research opportunity in genetics .interrogating several hundred thousand snps across many subjects at the same time raises many statistical challenges in the design and analysis of these studies .genotyping on such a scale requires new methodology for handling data quality issues ; likewise , association tests are computed for hundreds of thousands of markers , whose results have to be adjusted for multiple comparisons .the magnitude of these problems raises the question of whether the new technical ability to genotype such dense snp sets will translate into the identification of novel genetic disease loci or whether the technical advance remains under - utilized .a popular way to address the multiple testing in genome - wide association studies has been to design studies with a sample size of several thousand subjects that are large enough that realistic effect sizes can be detected , assuming that the test results will be corrected for multiple testing using the bonferroni approach .however , such large studies come at a price . by putting together samples of several thousand subjects , phenotypic and genetic heterogeneitywill be encountered in the sample .further , since the need for large sample sizes also influences the study - design choice , the most commonly used design choice is a case - control sample of unrelated individuals with minimal or no covariates .another popular approach is a population - based design of unrelated individuals without ascertainment condition related to the outcome of interest ( e.g. , studying obesity in a general population sample ) . in any event ,the ascertainment of subjects and collection of their phenotypic data is rarely carried out specifically for the gwas ; rather , the expense of the genotyping has led investigators to rely on samples previously collected and phenotyped for other studies , in some cases , large family samples that have been previously collected for other genetic studies .although the cost of genotyping is dropping rapidly , the cost of genotyping still tends to drive study design and make power considerations very crucial in the design .an alternative approach to population - based or case - control studies of unrelated individuals is family - based studies .family - based studies were used in association studies originally to provide protection against spurious association arising with population substructure .family designs offer some unique advantages at the design and analysis phase of a gwas .their complete robustness against heterogeneity at a phenotypic and genetic level allows the joint analysis of arbitrarily large and diverse samples with family designs , an advantage in the gwas setting . as we will discuss in section [ sec3 ], they have both drawbacks and benefits over conventional designs when genotyping errors are present .we will also discuss two - stage test strategies for family designs that maintain the original robustness of the approach , while achieving power - levels that are similar to those of population - based studies .our objective in this paper is to first describe some of the special features of family - based designs that make them attractive for association studies , then focus particularly on their use in gwas s with regard to genotyping errors and potential for addressing the multiple comparison problem .it has long been recognized that various sorts of population substructure can distort tests of association because different populations may have different disease rates , and/or genotype frequencies( ; pritchard , stephens and donnelly ( ) ; ) .family designs for genetic association studies were originally suggested ( ; ; ) as a way of avoiding spurious association due to population substructure .the classic paper by speilman , mcginnis and ewens ( ) on the transmission disequilibrium test ( tdt ) has contributed much to their general popularity .there are many variations on the family design , but the simplest and generally most powerful design consists of selecting affected offspring and their parents , and genotyping the trio .essentially , having the genotypes of the parents enables one to take advantage of `` mendelian randomization '' to avoid the need for an explicit control group . under the null hypothesis of no association between the disease and the marker ,each parent transmits one of their two alleles to each offspring , at random with probability 50/50 and independently of the other parent and of any other offspring .for the example in figure [ fig1 ] , the mother can only transmit the a allele , but the father can transmit either a or b with probability 50/50 .this holds whenever there is no selection of the offspring related to the marker in question .thus , when the parent s genotypes are known , one can easily calculate the distribution of the offspring genotypes under  .this distribution is used to construct tests of the null hypothesis .the observed and expected counts can be used to construct an asymptotic  test ( ; ) or exact tests can be used ( ) . because parents transmit independently to different offspring ,multiple affected siblings can be used , resulting in a potential savings in genotyping costs . with more common diseases , using transmissions to unaffected siblings may also be beneficial( ) .a more precise statistical argument regarding the robustness of the family designs can be made by considering the basis for the tdt test .the simple tdt test is a score test , based on the likelihood of the offspring genotypes , conditioned on the offspring trait and the parental genotypes ( ) . to develop this likelihood in a general setting ,let denote the parental genotypes of a trio , denote the trait of the offspring ( here the trait can be arbitrary ) , and let denote some numerical coding for the offspring genotype , for example , number of a alleles or a dummy variable coding for a recessive or dominant genetic model .further , let denote the probability density of the offspring trait , conditioned on the offspring genotype , the parental genotype and a vector of unknown parameters , . in genetic terminology, is the penetrance function and specifies the genetic disease model .generally , is assumed not to depend directly on the parental genotypes when offspring genotypes are in the model , but we leave them in for generality .the vector will contain both association parameters , say , , and nuisance parameters , say , , which will describe other aspects of the trait distribution .in particular , we parameterize so that , and under the null , , so that , that is , the distribution of the trait does not depend on the marker genotypes of the offspring under the null . further , let be the probability density of the offspring genotype conditioned on parental genotype .note that the latter is completely known and determined by mendel s laws , whereas the former reflects our alternative hypothesis , and is generally = 1  the conditional likelihood for the offspring genotype given parental genotypes and the offspring trait is given by \\[-8pt ] & & { } \big/\sum f(y|x , p , \theta ) f(x|p),\nonumber\end{aligned}\ ] ] where summation is over all compatible with .an important feature of conditioning on is that any nuisance parameters in the distribution of the parental genotypes , such as allele frequencies and random mating assumptions , are not needed . as noted above , the penetrance functiondoes not depend on under the null , and hence cancels out of the likelihood .thus , the distribution of under the null is given simply by , which is completely determined by mendel s laws ; no assumptions need be made about the distribution of parental genotypes or about the phenotypes .thus , a score test will have the correctly specified null distribution as long as mendel s laws hold , and will be completely robust to not only population substructure , but to potential misspecification of the trait distribution as well .  in the tdt , we condition on andlet denote the number of a particular allele that an individual has .the model can take any form , logistic , log - linear , linear , etc . , with modeling the probability for .a simple form for the penetrance function , which provides a generalization of the tdt for any phenotype , can be obtained by assuming an exponential family model for the trait distribution with a generalized linear model for the mean response ( ; ; ) . in this case, the score takes the special form of a type of covariance between the trait and the marker : [ x - e ( x|p ) ] , \ ] ] where summation is over all trios . here is the mean trait under and may depend upon the unknown nuisance parameters , and is computed using only mendel s laws .an asymptotic ( or ) test statistic is formed by normalizing ( [ eq2 ] ) by the square root of , where can also be computed simply from mendel s laws . alternately , exact tests usingmendel s laws to compute can be easilycalculated ( and ) .  a potential barrier to constructing score tests in this general caseis in estimating the nuisance parameters .standard likelihood ratio methods can not be used here , because under the null , the likelihood does not depend on and the parameters can not be estimated .the case of trios , where all offspring are affected , is special in this regard . here, is constant for everyone , and because we condition on , the score test can be reformulated as .\ ] ] it is easily seen that this score test yields the tdt when is coded to count the number of alleles of interest ( ) .  if we include unaffected offspring as well as affected , then equation ( [ eq2 ] ) still holds , but the test now depends upon estimating the prevalence because is not constant . if selection of subjects depends upon disease status , then prevalence can not be estimated from the sample data , but often some a priori information is available . in the more general case of measured phenotypes ,the test depends on the specified disease model via the nuisance parameters implicit in and remains valid regardless of choice of disease model provided mendel s laws hold .while model choice can affect power ( ; lange , demeo and laird ( ) ) , choice of the wrong disease model does not affect robustness , as the test is conditioned on the trait .when samples are selected on the basis of the disease trait , as is generally the case with dichotomous traits , the nuisance parameters can not be estimated from the data ; methods for specifying have been suggested ( ; ; ; ) .missing parental genotype information is a common problem , especially for later onset diseases.there have been several approaches suggested for handling missing parents , including estimating amodel for the parental genotypes distribution , and using joint likelihood ratio tests ( ) or using score tests which average over the estimated distribution of the parental genotypes ( ) for families with missing parents .these approaches are not guaranteed to retain robustness to population substructure , especially since both approaches generally make simplifying assumptions concerning the distribution of the parental genotypes ( e.g. , common allele frequencies and hardy weinberg equilibrium ) ; see .alternatively , when siblings are sampled , can be replaced in the above equations by , where denotes the sufficient statistic for parental genotype( ) .being the sufficient statistic , again does not depend upona model for parents genotype distribution , and the score test remains fully robust .the distributions are simple to enumerate , and tests based on ( [ eq1])([eq2 ] ) with replaced by if parents are notavailable can be implemented in the fbathttp://www.biostat.harvard.edu/\textasciitilde fbat/[www.biostat.harvard.edu/~fbat/ ] or pbat http://www.biostat.harvard.edu/\textasciitilde clange[www.]http://www.biostat.harvard.edu/\textasciitilde clange[biostat.harvard.edu/~clange ] software packages .however , the power of these tests can be much reduced , depending upon the number of additional siblings available .we refer to the fbat test to describe this general class of score tests which extends the tdt to other traits and other family designs .  in summary , conditioning on both the parental genotypes andthe offspring traits ensures robustness against misspecification of the disease model , and to the distribution of offspring genotypes under the null .the general approach has been extended to handle multiple siblings ( and ) , missing parents ( ) , multiple traits ( ) ,  haplotypes ( ) and multiple markers ( ; ) .   by and large ,most approaches for analyzinggwas studies , conventional or family designs , begin by testing each marker separately , and then do an adjustment for multiple comparisons to determine genome - wide significance and/or select promisingsnps or regions for further study based on rankings of some sort .there have been several proposals for alternative methods of testing to increase power in the face of multiple testing , as we will discuss in section [ sec5 ] , but , by - and - large , the genome - wide power of a gwas is usually estimated by calculating power for a single marker , using some appropriate alpha - level to adjust for multiple comparisons ; thus , comparative power issues for single markers translate directly to power calculations for genome - wide studies .we note that this one - marker , one - test approach is in strong contrast to genome - wide linkage scans , where one can at least approximate the null distribution of the test statistic across the genome , for example , maximized lod - score , under the null hypothesis of no linkage ( ) . with dense association scans ,the unknown pattern of ld precludes specification of the joint distribution of the association test statistics under the null of no association . in principle , using permutation tests in case - control studies can considerably improve the probability of at least one positive finding , but the magnitude of the computations are prohibitive in a gwas with hundreds of thousands of snps .an exception to the one test per typed snp are methods which incorporate information from the hapmap to impute non - typed snps , gaining additional power via testing a denser marker set ( ) .thus far , this approach has been limited to case - control data and investigation of methodology for family designs is desirable .family - based tests , being conditional tests , are robust and essentially model free , but the price of such robustness is some cost in terms of power .there are some cases , and some designs , however , where the power is essentially equivalent , as was shown for rare disease and the additive model in laird and lange ( ) . herewe consider power comparisons for the recessive model with an -level of 0.00001 to more nearly reflect a gwas testing situation .figures [ fig2 ] and [ fig3 ] compare the power of four different designs : case - control , trios , discordant sib pairs ( dsp ) and discordant sib trios ( dst ; at least one discordant sib pair and one other sibling ) , for a rare disease and a common one .the odds ratio is 1.75 in both cases , and the number of affected ( 1500 ) is the same for each design , although number of genotypes required can be different depending on design .the dsp design is always very inefficient , whereas dst can do well with more common disorders . for the recessive model ,the power of the case - control design and the trio design are virtually identical for common diseases ( e.g. , prevalence 14% ) , with minor advantages for trio designs for low allele frequency and minor advantages for the case - control design for common alleles . however, for rare diseases , the trio design is much for powerful than the case / control design .the reason for the relative power loss of the case / control design is that , for rare diseases , the differences between the genotype distribution of healthy controls and the genotype distribution of the general population are minimal and the contribution of the controls to the power of the test statistic diminishes . for the trio design , we use only cases and , consequently , such designs do not suffer this relative power loss for small prevalences .the power results for the trios differ slightly by prevalence because we base our model on the odds ratio rather than the relative risk model .we provide some simple algebraic calculations in the to illustrate this point .the large amount of genotyping required for a gwas is accomplished via specially designed genotyping platforms commonly called snp - chips .genotyping errors include several types of failures that can occur in the genotyping process ; these can result in either missingness and/or misclassification of genotypes . the raw data of a single genotype for a single individual is a pair of measured intensities for each allele ; the intensities are translated into genotypes , generally using some type of statistical clustering algorithm , referred to as the ` genotype calling algorithm . ' perhaps due to poor dna quality or design issues of the snp - chip , the sample may simply fail to provide intensities or the intensities do not separate into the three possible genotype clusters , making it impossible to obtain called genotypes .these errors all give rise to missing genotypes .further missingness arises in the data cleaning process which is described below .misclassification occurs if the calling algorithm makes a genotype call which is not correct ; the probability for misclassifying a genotype generally increases with lower minor allele frequencies , and can depend upon the true , unobserved , genotype .  in the data cleaning step of a gwas ,basic statistical analysis tools are used as quality control filters to identify snps and probands for which the snp - chip is not able to provide sufficient genotyping quality ( ) .such analysis techniques / filters include tests for departures from the hardy weinberg equilibrium , removal of snps with low frequencies or low `` call rates , '' or deletion of individuals with low call rates .since the inclusion of snps and probands with misclassified genotypes can lead to a substantial reduction in power , the data cleaning / filtering step is one of the most important parts in the analysis of a gwas .while there has been much progress in improving genotype calling and data cleaning algorithms , we can expect that there will continue to be some level of missing and misclassification in all gwas s .  when family data are used , an additional quality - control filter that is applied in the data cleaning step is the removal of mendelian inconsistencies .mendelian inconsistencies are genotype configurations in families that violate mendel s law . for example , if a `` b''-allele is observed in a subject whose parents do not carry any `` b''-alleles , this is an obvious violation of mendel s law .such genotype configurations are excluded from the analysis . furthermore ,if mendelian inconsistencies are more frequent for certain markers or families , this suggests that there are fundamental problems with the genotyping for these markers / families and , it is common practice to exclude them from the analysis altogether .markers and/or families with more than five mendelian inconsistencies are generally removed from the analysis .for population - based designs , the presence of genotyping errors resulting in either misclassification and or missing genotypes does not cause bias under the null provided errors / missingness is non - differential in cases and controls . by non - differential , we mean errors occur irrespective of case or control status .genotyping cases and controls separately can lead to differential genotyping errors , and considerable bias in association tests . with non - differential genotyping errors ,there is no bias under the null and the effect of the genotyping error is to simply decrease the overall power of the gwas for the population design .  for family - based designs ,the effects of genotyping errors are different .it is a well described phenomenon in the literature ( , ; ; sobel , papp and lange ( ) ; ) that genotyping errors can cause biased tests with inflated significance levels . with families , it will be possible to identify some of the misclassified genotypes by verifying that the offspring s genotype is not plausible based on the parental genotypes ( or in some cases , sibling genotypes ) .however , by removing families with transmission inconsistencies from the association analysis , only a fraction of the genotyping error is eliminated from the analysis . in the computation of the test statistic , this causes a seeming over - transmission of the major allele , which leads to the anti - conservativeness of the family - based association test .thus , while population - based studies have reduced power in the presence of genotyping errors , family - based studies will , in addition to that , have inflated pre - specified significance levels . to judge the relative importance of this fundamental difference between the two study - design types , it is important to consider the main purpose of gwa studies .their goal is the discovery of new genetic disease loci and their confirmation / replication in independent studies / samples .this is typically achieved by selecting the markers with the smallest -values from the gwa and trying to confirm / replicate them in independent studies .it is obvious that the presence of genotyping errors will reduce the overall power of both design types , either because of reduced power ( case - control ) or by both reduced power and inflated type-1 error ( family designs ) .however , it is unclear for which design type these effects are more deleterious and careful simulation studies are much needed to address this issue .  in practice , it will be important to estimate the undetected genotyping error rate in the data in order to assess the reduction in overall - power of the gwa study that is attributable to this error source .otherwise , if a gwa is unable to identify new loci , it is unclear whether this is due the actual absence of genetic risk loci or due to the reduction in overall power caused by poor genotyping quality .family - based studies offer a unique possibility to estimate the undetected genotyping error rate . by looking at the transmission pattern of the common allele for all genotyped markers in a gwa study ,an overall / genome - wide fbat statistic can be computed and the undetected genotyping error rate in the study can be estimated through simulations under various error models ( ) .with mapping arrays for more than one million snps now available ( ; ; ; ) , genome - wide association studies carry the promise to identify replicable associations between important genetic risk factors and most complex diseases .one of the major hurdles that needs to be addressed in order to make genome - wide association studies successful is the multiple comparison problem .hundreds of thousands of snps are genotyped and examined for potential associations with multiple phenotypes , possibly using different model assumptions , resulting in potentially millions of statistical tests .initial efforts to resolve this problem with case - control designs were directed toward multi - stage designs involving multiple independent samples . at stage 1 ,all snps are tested in a relatively small sample and the most significant ones retained for testing with a larger , independent sample ; the winnowing process can be repeated multiple times. however , skol et al .( ) showed that such designs are inherently less powerful than designs which use all samples for the final analysis of selected snps , even though bonferroni adjustment must be made for testing all snps .thus , the desired strategy now for population based designs is to select a large enough sample ( 35000 cases and an equal number of controls ) to achieve sufficient power for all snps simultaneously , but also utilize independent `` replication '' samples which are different from the original sample in some distinct way , for example , non - overlapping populations .other strategies to ameliorate the multiple comparisons problem utilize some `` outside '' information , for example , information from linkage studies , functional snps , etc .such approaches include bayesian approaches which use prior distributions to specify effects for markers ( ) , weighted bonferroni methods which assign different significance levels to each snp according to their `` importance or relevance '' ( roeder , devlin andwasserman ( ) ; ) and split - sample approaches ( ; ) . for family - based associationtests ,the idea of using `` outside information '' naturally translates to the use of the information about the association at a population - based level that is not utilized in the family - based association test .a general approach to two - stage testing for family designs builds on the two information sources about association that are present in family - based designs . using the notation introduced in section [ sec2 ] for the distribution of and ,the joint distribution for , and ( or , equivalently , ) can be partitioned into two statistically independent components ( ) , where represents additional parameters required to model the parental genotype distribution , for example , genotype frequencies and possible non - random mating .note that both components , and will have information about , but the information from , will depend on the parental genotype distribution , and can be sensitive to population substructure .for the first step of the testing strategy , the screening step , we use the information in , to estimate the association parameters ; the second , or testing step , uses .the likelihood decomposition implies that both steps of the testing strategy are independent .the `` evidence for association '' estimated from can be utilized in the testing stage , without having to adjust the test for the estimation of the genetic effect size in the first stage .several methods have been suggested to exploit this relationship in developing testing strategies which use both forms of information in order to increase power , while retaining robustness of the test .van steen et al .( ) originally proposed a version of this two - step testing strategy for the analysis of quantitative traits .first , an effect size is estimated for each snp by regressing the offspring phenotype on ; this effect size is used to calculate the estimated power of the fbat statistic for each snp ( ) .some number of top ranking snps ( 10 or 20 ) were selected for testing with the fbat statistic at the second stage .because of the independence , both steps can be applied to the same data set without having to adjust the overall significance level for the multiple usage of the data . an extension by ionita - laza et al .( ) proposed testing all snps at the second stage using weighted bonferroni .extensions of this testing strategy are available for using parental phenotypes and arbitrary structures at the screening stage ( ) and for case / control designs ( ) .the van steen approach has three key advantages : ( 1 ) the method achieves statistical power levels which can be substantially higher than those of standard family - based approaches and is thereby able to establish genome - wide significance withsmaller / more realistic sample sizes ( ; ; feng , zhang and sha ( ) ; ) .( 2 ) the van steen algorithm maintains the separation between the multiple testing problem and the replication process .replication attempts in different studies are reserved for the generalization of the established associations and the assessment of heterogeneity between study populations .( 3 ) since genome - wide significance is established in the first data set , the number of snps that is pushed forward to true replication in other populations is generally very small and does not require a large budget , which makes simultaneous replication attempts in multiple samples feasible .extensive simulation studies have shown that 2-stage testing strategies that utilize both sources of information about the association can help family - based studies to achieve power levels that are similar to those of population - based studies , while maintaining the original advantages of family - based study , that is , complete robustness against confounding .  by looking at the distribution of parental mating types in ascertained samples , murphy et al .( ) extended the general approach to the trio - designs in which all probands are affected . even here , the application of 2-stage van steen - testing strategies can lead to meaningful power improvements over the standard tdt .other possibilities for utilizing the information from the screening step include specifying `` tuning - parameters '' in the fbat - statistic ( ; ) so that the power of the fbat test is maximized .family designs have historically been popular because of their robustness to population substructure .an additional , often unappreciated , feature of family - designs which is important with measured or time - to - onset outcomes is their robustness to model specification , and the ability to utilize the population to specific unknown parameters in the model . with the availability of modern snp chips , and genotyping of thousands of subjects on hundreds of thousands of markers, we now have the potential to identify the genetic backgrounds of individuals , and utilize that information to control for confounding by population substructure in case - control studies ( ) .an important question is whether or not there is a need for family designs in the era of gwas , given the potential to resolve difficulties with population substructure in case control designs .additional studies and experience with actual studies are needed to compare the performance of family designs and adjusted case - control designs in gwas settings .  hampered by limitations in terms of power in many scenarios , and by the difficulty of recruitment , family - based designs certainly can not be considered as the gold standard approach in genome - wide association studies .however , given the unique properties and features of a family design , they will continue to play a pivotal role in large scale association studies .  in multi - stage genome - wide association studies ,family - based studies should be utilized as one of the stages as early as the budget permits its implementation .their complete robustness against both genetic confounding and misspecification of the phenotypic model provides them with an important role in the process of replicating and validating findings of the discovery step .given the unavoidable genetic and phenotypic heterogeneity in large - scale multi - stage genome - wide association studies , this feature of family - based association tests is crucial and should not be ignored .if the budget permits the additional genotyping cost , family - studies can be a favorable choice for the first stage of a genome - wide association study . there ,family - based studies can be designed so that they have equivalent power topopulation - based studies and , at the same time , offer a unique combination of additional analysis features and robustness properties .  while the analysis features of family - based designs make them an attractive choice in the design phase of genome - wide studies , their abilities to assess the magnitude of the hidden genotyping errorshould always be utilized , even with case / control designs . by genotyping a small number of families on the same platform with the case / control samples, researchers can examine the genotyping quality of the data after the qc process and assess the true power of the study .here we do some simple calculations which illustrate the power differences between case - control and trio designs .the basic idea is to calculate the expected value of the corresponding statistics under the alternative . to make the calculations simple, we use a relative risk model , and we assume that allele frequency , the relative risk and prevalence are small .we use the following notation :  disease allele frequency ,  relative risk ,  prevalence , , where indicates disease , and indicates the recessive genotype . assuming the hardy weinberg equilibrium holds in the population , and .  for the case - control design ,we compute \\[-8pt ] p_{\mathrm{controls } } & = & p(x=1|y=0)\nonumber \\ & = & ( 1-r\rho ) p^2 /(1-k),\nonumber\end{aligned}\ ] ] and letting , we have that the expected is approximately where is the number in each group . for , , and ,this gives , and , which corresponds to the notion of zero power if .for the trio design , we consider the 2 informative mating types , that is , 2 heterozygous parents ( type 1 ) and one heterozygous parent and one rare homozygous parent ( type 2 ) . under the alternative hypothesis ,the expected number of families for each mating type can be calculated by   ,   .next , we compute the mendelian residuals which are defined as the expected marker score under the alternative hypothesis minus the expected marker score under the null - hypothesis for both mating types :   ,   .  the variance of the mating - types used in the denominators of the fbat statistics are given by 3 and 1 respectively .then the expected fbat - statistic for a recessive model under the alternative hypothesis is given by for the parameters given above , this equals , which results in the observed power levels of the plot for .supported in part by a grant from the national institute of mental health .di , x. , matsuzaki , h. , webster , t. a. , hubbell , e. , liu , g. , dong , s. , bartell , d. , huang , j. , chiles , r. , yang , g. , shen , m. m. , kulp , d. , kennedy , g. c. , mei , r. , jones , k. w. and cawley , s. 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Abstract: genome - wide association studies ( gwas ) offer an exciting and promising new research avenue for finding genes for complex diseases . traditional case - control and cohort studies offer many advantages for such designs . family - based association designs have long been attractive for their robustness properties , but robustness can mean a loss of power . in this paper we discuss some of the special features of family designs and their relevance in the era of gwas .  .