PERSONAL COMPATIBILITY USING HLA

Processing personal compatibility matches includes a computer system receiving input of a first allele group determined to be present in a first person and a second allele group determined to be present in a second person. The first allele group and the second allele group are members of a set of allele groups for a human leukocyte antigen (HLA) gene. Each group of the set of allele groups is predefined to contain related alleles. By comparing the first allele group to the second allele group, a similarity of the first allele group to the second allele group may be determined. An indication of personal compatibility of the first person and the second person is inversely related to the determined similarity. The computer system can output the indication of personal compatibility to, for example, a matchmaker or a third-parting service.

FIELD

This disclosure relates to obtaining and processing genetic information.

REFERENCE TO SEQUENCE LISTING

A sequence listing in an ASCII text file (IC-HLA_ST25.txt) accompanies this specification, as provided for by the EFS Legal Framework Notice of Apr. 6, 2011. The file was created on Jun. 19, 2013 and contains 21,741 bytes. The entire content of the sequence listing file is hereby incorporated by reference.

BACKGROUND

Personal compatibility is important to long-term and satisfying relationships. There are many known techniques for increasing the likelihood that two people will find themselves to be compatible, and some of these techniques analyze genes. However, known techniques do not generally teach quantified genetic comparisons that can succinctly or accurately express the relatively complex differences between genes of the two individuals.

SUMMARY

According to one aspect of the present disclosure, a method of processing personal compatibility matches includes receiving input of a first allele group determined to be present in a first person and a second allele group determined to be present in a second person, the first allele group and the second allele group being members of a set of allele groups for a human leukocyte antigen (HLA) gene. Each group of the set of allele groups is predefined to contain related alleles. The method further includes comparing the first allele group to the second allele group to determine a similarity of the first allele group to the second allele group, and outputting an indication of personal compatibility of the first person and the second person. The indication of personal compatibility is inversely related to the similarity.

According to another aspect of the present disclosure, a computer system includes at least one server configured to receive input of a first allele group determined to be present in a first person and a second allele group determined to be present in a second person. The first allele group and the second allele group are members of a set of allele groups for an HLA gene. Each group of the set of allele groups is predefined to contain related alleles. The server is further configured to compare the first allele group to the second allele group to determine a similarity of the first allele group to the second allele group, and to output an indication of personal compatibility of the first person and the second person. The indication of personal compatibility is inversely related to the similarity. The system further includes a remote computer connected to the server via a network. The remote computer is configured to output the indication of personal compatibility.

DETAILED DESCRIPTION

The present disclosure is directed to assessing personal (e.g., romantic or sexual) compatibility between two human beings based on a quantified comparison of the human leukocyte antigen (HLA) genes of the two individuals. The quantified comparison uses primers and calculation to arrive at a compatibility indication, which can succinctly and accurately express the relatively complex differences between HLA genes of the two individuals.

FIG. 1illustrates a method of processing personal compatibility matches, such as romantic compatibility matches between two people.

An interested person18provides a DNA sample20by, for example, taking a cheek swab22. A kit can be provided for this purpose, and such a kit can include a buccal swab and a sample container. Once the person has undergone the swab, the swab can be inserted into the sample container, which can then be mailed or otherwise delivered to a laboratory for processing. The kit can also a include instructions on how to perform the swab and a prepaid mailing envelope addressed to a suitable laboratory. More than one swab kit can be provided, so that more than one sample20can be taken, as a contingency against defective samples. When there is a matchmaker involved, swab kits may be provided to the matchmaker along with training, so that the matchmaker can request and instruct clients to perform a swab. Other techniques for obtaining DNA samples are also contemplated, such as hair samples, blood samples, skin samples, or samples of other DNA-bearing body tissue.

The laboratory performs DNA extraction and probing24for HLA genotyping of the interested individual, which is discussed in detail below. The HLA system contains genes related to immune system function in humans. Further, it has been discovered that HLA genes also play a role in human biological compatibility, in that the major histocompatibility complex (MHC) contributes to body scent in humans. Body scent and taste affect human sexual attraction and may help people, perhaps subconsciously, to find partners. Moreover, couples in long-term relationships tend to have MHC genes dissimilar to each other. Hence, the DNA extraction and probing24are performed on interested individuals to determine genetic information for HLA genes with the aim of determining HLA dissimilarity between two individuals so as to quantify the HLA aspect of attraction or compatibility between the two individuals.

HLA information resulting from the DNA extraction and probing24is provided to one or more server computers26connected to one or more computer networks30,34. The networks30,34can be the same general network, such as the Internet. The server26and any connected remote computers or other electronic devices that receive output from the server26may be known as a computer system. The server26is configured to store and output the HLA information, and to process matches between selections of two individuals, one female and one male.

The server26can be configured to provide output of the interested person's HLA information28to the interested person18. This can be performed by the server26outputting the interested person's HLA information28over the network30to an application at a computer or other electronic device32operated by the interested person18. For example, the server26can output such information at a web page available to the interested person18via a log in or other credential verification.

The server26can be configured to provide output of an indication34of personal compatibility of two individuals based on the determined dissimilarity of their HLA genes. Output of the personal compatibility indication36can be provided over the network34in a form suitable for use by a computer or other electronic device operated by a third-party, such as a matchmaker38. An example of output suitable for use by the matchmaker38includes a web page available to the matchmaker38via a log in or other credential verification. Alternatively or additionally, output of the personal compatibility indication36can be provided over the network34in a form suitable for use by a third-party service40, such as a dating website or similar online personal service. An example of output suitable for use by the third-party service40includes a database connection to the server26. Alternatively or additionally, output of the personal compatibility indication36can be provided to one or two of the interested persons in a romantic couple or potential couple (e.g., two people contemplating marriage).

The server26can be configured to restrict detailed HLA information to being output to the individual that provided the information, so as to protect privacy. This can be achieved by providing unique access codes42with the buccal swab kits, such as printed on a sticker or tag attached to the sample container. The unique access codes42are associated, at the server26, to individual accounts assigned to interested persons18. After a person's swab is processed, the HLA information is entered into the data store at the server26and associated with the unique access code42, so that the person can then log in to their account to view their own HLA information. This can advantageously prevent the matchmaker38, third-party service40, or another entity from obtaining the private genetic information of the interested persons18.

The server26can be configured to restrict personal compatibility indications36between two interested persons18to being output to one or more of the matchmaker38and the third-party service40. This can be achieved by the server26storing and outputting personal compatibility indications36as indexed to non-private identities of two interested persons18, such as two individual profile IDs, a match ID, or similar index. Such non-private identities are isolated from the unique access codes42, so that knowledge of one or more non-private identities cannot be used to obtain a unique access code, and knowledge of a unique access code cannot be used to obtain other information. This can advantageously prevent an interested person18from obtaining information about potential matches outside of the context of the matchmaker38or third-party service40.

FIG. 2shows an example data structure suitable for the server26, particularly when matchmakers38are involved. The data structure can be implemented using database tables, queries, files, or similar. Lines connecting elements inFIG. 2can be understood to represent relationships between sets of elements, such as local and foreign key relationships between database tables.

HLA information52for various different individuals is stored as indexed by unique access code50, as discussed above. After DNA processing, the resultant HLA information52can be uploaded or entered by an administrator of the server26.

Personal information about interested individuals, such as name60, sex62, age64, and general location66(e.g., city, state/province, etc.) are stored indexed by profile ID54. Photographs of individuals may also be stored. Access to individual profiles can be managed by credentials such as a password56. Each profile can further store contact information for the interested persons, such as email address68, phone number70, and physical address72. Each profile can further store a unique access code74for use as a foreign key for access to the HLA information52. Accordingly, each interested person can access their profile to obtain their personal HLA information.

Match information can be stored as indexed by match ID76. Each match or potential match is tied to an individual matchmaker ID78and identifies two profiles by way of first and second profile IDs80,82, which act as foreign keys. A match score84, once determined, can be stored for each match.

Matchmakers can be provided with access to the data by way of matchmaker profiles indexed by matchmaker ID86. Matchmaker profiles can be managed by credentials such as a password88, and may store contact information for the matchmaker, such as an email address90. Matchmaker ID86can be used as a foreign key to access matches and potential matches.

The server26can be configured to allow a user logged in as a matchmaker to create profiles for interested individuals, and such profiles may be tied to the matchmaker's account as a client. For this purpose, matchmaker profiles may further store a list of client IDs92that map to the profile IDs54of the interested persons.

The server26can be configured to allow a user logged in as a matchmaker to run queries on the profiles of interested persons, so as to form matches and potential matches. Such queries can make reference to the HLA information52stored for interested individuals, as will be discussed in further detail below. The result of such a query is an indication of compatibility that is inversely related to HLA similarly.

In some embodiments, the server26is configured to limit a matchmaker to running queries only on those profiles listed under that matchmaker's client IDs92. In other embodiments, a matchmaker can run queries referencing one profile listed under that matchmaker's client IDs92and another profile irrespective of whether such profile is listed under that matchmaker's client IDs92. In still further embodiments, no such limitations are placed on the profiles that may be selected by the matchmaker. In the latter two cases, the server26can be configured to prevent the matchmaker from obtaining the contact information of the interested persons whose profiles are not listed under that matchmaker's client IDs. Rather, the server26can forward contact inquiries for such an interested person to the respective matchmaker who has the respective profile listed under his/her client IDs92.

The server26can further be configured to notify interested individuals by, for example, sending an email message when HLA information52has been processed and is available for viewing. Similarly, the server26can be configured to notify the respective matchmaker, again, for example, via email, when the HLA information52is available for performing comparisons with other interested individuals.

FIG. 3shows an example data structure suitable for the server26, particularly when external third-party services40are provided access. The data structure can be implemented using database tables, queries, files, or similar. Lines connecting elements inFIG. 3can be understood to represent relationships between sets of elements, such as local and foreign key relationships between database tables. The description forFIG. 2can be referenced for like-numbered elements.

In this data structure, profiles are reduced to a profile ID54, password56, and unique access code74for HLA information. Thus, the interested person can log in to view their own HLA information based on the access code provided with the swab kit.

Queries can be constructed to determine compatibility between two individuals represented by profiles IDs80,82. Such queries can be performed in response to a request received at the server26from a third-party service40that has knowledge of the profile IDs80,82or other indicating information. The server26can track a query based on a remote query ID85associated with the profile IDs80,82and respond with a match score84indicative of the degree of HLA dissimilarity of the individuals represented by the profile IDs80,82. For example, a third-party service40may make a request for a match score84for profiles IDs80,82, with the request accompanying a remote query ID85, such as a hash or other relatively unique identifier. The server26, in response, determines the match score84using the techniques described herein, and responds to the third-party service40with the match score84and the remote query ID85. Accordingly, the third-party service40can be configured to accept input from interested individuals of their profile ID54, as stored by the server26, so that the third-party service40can generate and send such queries to the server26.

FIG. 4illustrates data and operations at the server26for processing matches.

A predefined set of allele groups100for one or more HLA genes is defined to contain related alleles for each HLA gene considered. In some embodiments, one or more of the HLA-A, HLA-B, and HLA-DRB1 genes have allele groups defined by the set of allele groups100. In other embodiments, all of the HLA-A, HLA-B, and HLA-DRB1 genes have allele groups defined by the set of allele groups100. Each of the allele groups100can be assigned a name, number, or other identifier.

Relationships102for the predefined set of allele groups100can be stored in a database table or other data structure suitable to be updated and maintained based on additions or refinements made to the allele groups. The relationships102can be indexed by allele group identifier and can express a similarity between two (or more) allele groups100. For example, a first allele group may be classified as similar to a second allele group, while the first allele group may be classified as dissimilar to a third allele group. The relationships102can express degrees of similarity using numerical values.

The allele groups present in two interested persons18(FIG. 1) are determined based on DNA extraction and probing24. Primers are defined for each of group of the set of allele groups100and used to identify of which groups the alleles present in the first and second interested persons are members. That is, each of the allele groups100is defined by one or more primer sets, and when such one or more primer sets binds to DNA extracted from a person's DNA sample to amplify the corresponding allele, the person is considered to have the respective allele group. The primers can be single nucleotide polymorphism (SNP) primers designed to be specific for a minimum of one nucleotide difference between sequences. For each HLA gene considered, it is expected that each interested person will have two alleles, one for each copy of the gene that is carried, and that the two alleles will fall within one or two of the defined groups.

After the DNA extraction and probing24, the identifiers of the HLA allele groups determined to be present in the first person104and the identifiers of the HLA allele groups determined to be present in the second person106can be inputted into the server26and stored in a suitable data structure.

The server26is configured to execute a personal compatibility comparison engine108, which receives as input the HLA allele groups of the first person104and the HLA allele groups of the second person106. The engine108references the relationships102among the predefined set of allele groups100to determine an overall dissimilarity. The engine108, for each HLA gene considered, is configured to compare at least one first allele group present in the first person104to at least one second allele group present in the second person106to determine a dissimilarity of the first allele group to the second allele group. Referencing the relationships102, the engine108calculates a similarity based on the comparison and outputs an indication110of personal compatibility of the first person and the second person, with the personal compatibility being inversely related to the similarity.

For example, if the first allele group and the second allele group are the same, the engine108determines a maximum similarity and the personal compatibility indication110accordingly indicates that the first and second persons are relatively incompatible. If the first allele group and the second allele group are indicated by the relationships102to be highly dissimilar, then a low similarity factor is determined and the personal compatibility indication110indicates that the first and second persons are relatively compatible. Degrees of HLA gene dissimilarity and directly related degrees of romantic compatibility are available based on a number of allele groups100defined and their relationships102.

With reference toFIG. 5, a set of predefined allele groups120includes allele groups for all of the HLA-A, HLA-B, and HLA-DRB1 genes. In some embodiments, the set of allele groups120includes one or more of an A2′ group, an A9′ group, an A19′ group, an A10′ group, an A11′ group, and an A1′ group, which can be based on or the same as the respective known HLA-A2, HLA-A9, HLA-A19, HLA-A10, HLA-A11, and HLA-A1 serotypes. (Prime notation is used herein to illustrate that the known HLA definition of an allele group may be used, that a modified definition may be used, or that an entirely new definition may be used.) In some embodiments, the set of allele groups120includes one or more of a B50′ group, a B37′ group, a B40′ group, a B15′ group, a B44′ group, and a B55SG′ group, which can be based on or the same as the respective known HLA-B50′, HLA-B37′, HLA-B40′, HLA-B15′, HLA-B44′, and HLA-B55SG′ serotypes. In some embodiments, the set of allele groups120includes one or more of a DR2′ group, a DR4′ group, a DR9′ group, a DR11′ group, a DR14′ group, and a DR13′ group, which can be based on or the same as the respective known HLA-DR2′, HLA-DR4′, HLA-DR9′, HLA-DR11′, HLA-DR14′, and HLA-DR13′ serotypes. In some embodiments, two or more of the A2′, A9′, A19′, A10′, A11′, A1′, B50′, B37′, B40′, B15′, B44′, B55SG′, DR2′, DR4′, DR9′, DR11′, DR14′, and DR13′ allele groups are included in the set of allele groups120. In some embodiments, all of the A2′, A9′, A19′, A10′, A11′, A1′, B50′, B37′, B40′, B15′, B44′, B55SG′, DR2′, DR4′, DR9′, DR11′, DR14′, and DR13′ allele groups are included in the set of allele groups120.

In some embodiments, with reference to the attached sequence listing, the allele groups are defined by the following primers shown in Table 1, which can be provided in 6 wells of a polymerase chain reaction (PCR) primer array.

In other embodiments, with reference to the attached sequence listing, the allele groups are defined by the following primers shown in Table 2, which can be provided in 18 wells of a PCR primer array.

In still other embodiments, with reference to the attached sequence listing, the allele groups are defined by the following primers shown in Table 3, which can be provided in 45 wells of a PCR primer array.

In Tables 1, 2, and 3 above, each row represents a primer set that can be provided in wells of a primer array, such as a primer plate. In other embodiments, one or more of the primer sets listed (Table 1, 2, or 3) for each of the HLA-A, HLA-B, and HLA-DRB1 genes are provided in a primer array. In other embodiments, two or more of the primer sets listed (Table 1, 2, or 3) for each of the HLA-A, HLA-B, and HLA-DRB1 genes are provided in a primer array. In other embodiments, substantially all of the listed primer sets (Table 1, 2, or 3) are provided, where “substantially” can be taken to mean that one or more of the primer sets may be omitted provided that the calculations discussed herein are modified to compensate. This can be achieved in a manner similar to compensation for missing information, as also discussed herein. Generally, the more of the listed primer sets (Table 1, 2, or 3) that are used, the more accurate the results. However, omitting some of the primer sets does not make the results invalid. In still other embodiments, all of the listed primer sets (Table 1, 2, or 3) are provided in wells of a primer array. In any of these embodiments, additional primer sets may be added to additional wells.

The relationships122are similar to the relationships102discussed above. Further, the relationships122are illustrated by the condensed phylogenetic trees shown inFIGS. 6a-6c.FIG. 6ashows the tree for allele groups A2′, A9′, A19′, A10′, A11′, and A1′.FIG. 6bshows the tree for allele groups B50′, B37′, B40′, B15′, B44′, and B55SG′.FIG. 6cshows the tree for allele groups DR2′, DR4′, DR9′, DR11′, DR14′, and DR13′.

Similarity values for allele groups can be determined based on a number of potential combinations for allele groups with reference to a number of hops between two leaf nodes130on a particular tree. That is, with reference toFIG. 6a, there are six possible combinations (irrespective of order) of two allele groups that are the same (e.g., A2′-A2′, A9′-A9′, etc.) and hence the similarity value for any two same allele groups can be taken as this number of combinations, namely, 6. Similarly, there are five possible combinations of two allele groups that are one hop removed (e.g., A2′-A9′, A9′-A19′, A19′-A10′, A10′-A11′, and A11′-A1′), with example hops being illustrated in the figures in dashed line. Hence, the similarity value for any two allele groups that are one hop removed can be taken as 5. Likewise, there are four possible combinations of two allele groups that are two hops removed (e.g., A2′-A19′, A9′-A10′, A19′-A11′, and A10′-A1′) and the similarity value for any two allele groups that are two hops removed can be taken as 4. There are three possible combinations of two allele groups that are three hops removed (e.g., A2′-A10′, A9′-A11′, and A19′-A1′) and the similarity value for any two allele groups that are three hops removed can be taken as 3. There are two possible combinations of two allele groups that are four hops removed (e.g., A2′-A11′ and A9′-A1′) and the similarity value for any two allele groups that are four hops removed can be taken as 2. And finally, there is one possible combination of two allele groups that are five hops removed (e.g., A2′-A1′) and the similarity value for any two allele groups that are five hops removed can be taken as 1. In addition, it should be apparent that the total number of possible combinations is the sum of the above numbers, which in this example is 21. The same logic applies to determining similarity values for the HLA-B (FIG. 6b) and HLA-DRB1 (FIG. 6c) genes.

In other words, and to further illustrate the above logic, the number of hops between two leaf nodes130on a particular tree can be taken as a dissimilarity value for the two allele groups represented by the two leaf nodes130. For example, as shown in dashed lines onFIG. 6a, allele groups A2′ and A9′ are adjacent and thus one hop there-between results in a dissimilarity value of 1, allele groups A2′ and A19′ are separated by two hops and thus have a dissimilarity value of 2, and so on for each combination of two allele groups. For each combination of two allele groups, a similarity value can be calculated by subtracting the respective dissimilarity value from the total number of allele groups (or leaf nodes130). In the example shown, allele groups A2′ and A1′ have a dissimilarity value of 5 and thus have a similarity value of 1 (i.e., 6 allele groups for HLA-A minus the dissimilarity value of 5). The same applies to the HLA-B and HLA-DRB1 condensed phylogenetic trees ofFIGS. 6band6c.

It should be understood that similarity and dissimilarity are inversely related and determining a similarity is equivalent to determining a dissimilarity, provided that each are identified as such to the end consumer.

The relationships122can be numerically defined by tables of predefined similarity factors, as shown inFIGS. 7a-7c.FIG. 7ashows similarity factors for each possible combination of allele groups for the HLA-A gene. Columns indicate an allele group determined to be present in one of the interested persons, and rows indicate an allele group determined to be present in the other of the interested persons. The similarity factors in the cells are fractional values with similarity value in the numerator and a total number of possible combinations in the denominator. In the case of the six HLA-A groups discussed above, the total number of possible combinations is 21 noting that order is unimportant and repetition is permitted (i.e., two people may have alleles of the same group). Thus, the similarity factors represent a normalized relatedness of two alleles from the two interested persons. For example, if the first copy of the HLA-A gene in the first person is of the A2′ allele group and the first copy of the HLA-A gene in the second person is of the same group, then the similarity factor for those two alleles is 6/21. If, on the other hand, the first copy of the HLA-A gene in the second person is of the A19′ group, then the similarity factor is 4/21. The same applies to the HLA-B and HLA-DRB1 tables ofFIGS. 7band7c.

Referring back toFIG. 5, with the relationships122defined as above, the allele groups detected in each of the two persons can be used as inputs to a personal compatibility comparison engine124that references the relationships122, which can include looking up values from the tables ofFIGS. 7a-7c. That is, each copy of the first person's HLA-A genes are determined to belong to groups A(1,1) and A(1,2), where the first index represents the person and the second index indicates the gene copy. Likewise, the first person's HLA-B genes are determined to belong to groups B(1,1) and B(1,2), and so on for all elements of A( ) and B( ), with the same logic continuing and thus the second person's HLA-DRB1 genes determined to be of groups DR(2,1) and DR(2,2).

The personal compatibility comparison engine124can be configured to lookup similarity factors, using for example the tables ofFIGS. 7a-7c, for each combination of alleles of a particular HLA gene between the two interested persons being compared. That is, for the HLA-A gene, the first allele in the first person A(1,1) and the first allele in the second person A(2,1) are used to determine a similarity value from the table ofFIG. 7a. Likewise, the second allele in the first person A(1,2) and the first allele in the second person A(2,1) are used to determine a similarity value from the table ofFIG. 7a, and so on, until a similarity value has been determined for each of the four possible combinations: A(1,1) to A(2,1), A(1,2) to A(2,1), A(1,1) to A(2,2), and A(1,2) to A(2,2). The same is performed for the HLA-B and HLA-DRB1 genes. When each of the three HLA genes is defined to have six groups, as mentioned above, a total of 12 comparisons (four for each gene) are made and twelve similarity factors result. All of the similarity factors can then be multiplied together to obtain a combined similarity result indicative of a total HLA comparison of the two individuals. Where a function, f, is defined to perform a value lookup in the respective table (FIGS. 7a-7c) based on two inputted allele groups (one from each person),FIG. 8illustrates an example calculation of the combined similarity result.

The combined similarity result represents a probability of two people with that similarity or dissimilarity coming together. A lower probability equates to greater dissimilarity and thus a higher compatibility.

One advantage of the above calculation is that HLA relatedness is quantified in a way that allows for finer-grained analysis. Not only does the process detect the presence of certain allele groups within two interested persons, the process further determines a numerical relatedness based on combinational comparisons of the allele groups.

The personal compatibility comparison engine124can be further configured to reference indications of compatibility with reference to a calculated combined similarity result.FIG. 9illustrates a table of compatibility descriptions142and percentage ranges144based on similarity factor bins140. As can be seen, romantic compatibility increases with decreasing HLA similarity factor. The combined similarity result for two interested persons, as determined by the engine124, can be used as a key to determine the similarity factor bin140and thus the compatibility description142and percentage144for that couple.

Referring back toFIG. 5, one or more of the determined compatibility description142and percentage144can be output by the engine124as a personal compatibility indication126. The personal compatibility indication126can then be communicated to the matchmaker38or third-party service40(FIG. 1).

In an example, suppose it is determined that a first individual, James, has HLA genes having alleles that fall into groups A9′, A19′, B40′, B44′, DR2′, and DR13′, and a second individual, Barbara, as HLA genes of groups A2′, A1′, B37′, B40′, DR11′, and DR14′. Referencing the table ofFIG. 7a, A9′ and A2′ have a similarity factor of 5/21, A9′ and A1′ have 2/21, A19′ and A2′ have 4/21, and A19′ and A1′ have 3/21. Referencing the table ofFIG. 7b, B40′ and B37′ have a similarity factor of 5/21, B40′ and B40′ have 6/21, B44′ and B37′ have 3/21, and B44′ and B40′ have 4/21. Referencing the table ofFIG. 7c, DR2′ and DR11′ have a similarity factor of 3/21, DR2′ and DR14′ have 2/21, DR13′ and DR11′ have 4/21, and DR13′ and DR14′ have 5/21. The combined similarity result is then determined by the product (FIG. 8) of (5/21)*(2/21)*(4/21)*(3/21)*(5/21)*(6/21)*(3/21)*(4/21)*(3/21)*(2/21)*(4/21)*(5/21), which approximately equals 7.0E-10. The combined similarity result is then applied to the table ofFIG. 9to determine that James' and Barbara's compatibility is “Excellent” with a score of between 85 and 89.

The personal compatibility comparison engine124can also be configured to accommodate one or more missing allele groups from the two interested persons, which may result from lab errors, poor sample quality, or if the person carries an allele that is not detected. For each missing allele group, the engine124performs bounding calculations using the lowest possible values and the highest possible values for the missing allele group. The engine124can perform a separate calculation for each assumption for the group of the missing allele and take the largest and smallest values as upper and lower bounds. In the above example, suppose that Barbara's A2′ allele is missing. The engine124compares A9′ and A19′ to assumed identities of the missing allele and determines that the maximum similarity occurs when the missing allele is A9′ or A19′ (with similarity factors of 6/21 and 5/21) and the minimum similarity occurs when the missing allele is A1′ (with similarity factors of 2/21 and 3/21). Thus, two combined similarity results are determined using the equation ofFIG. 8, one using 6/21 and 5/21 and another using 2/21 and 3/21. The resulting combined similarity results of 10.6E-10 and 2.1E-10 can be averaged and then used to determine a corresponding compatibility (“Excellent” with a score of 85-89), or can be used to determine a corresponding compatibility range of “Excellent to Exceptional” with a score range of 85 to 90+. Compensating for missing DNA information can advantageously reduce the need to retake or reprocess DNA samples, which can be costly and time consuming.

FIG. 10shows example output of HLA information28(FIG. 1) for an individual interested person18. A web page150or output of another format includes appropriate descriptive text152and a table154of HLA results containing the detected allele groups for each gene examined.

FIG. 11shows example output of a personal compatibility indication36(FIG. 1) for two interested persons18, who may have each contacted a matchmaker. A web page160or output of another format includes appropriate descriptive text162and a table164containing one or more indications of compatibility, such as the determined combined similarity result (shown as a “score” and trimmed of exponent), a compatibility description142(FIG. 9), and a compatibility percentage144. A graphical indicator166may, alternatively or additionally, be provided as an indication of compatibility. In some embodiments, the graphical indicator166includes a linear bar on which the combined similarity result is plotted, and as such, the indicator166may include a plot point168, such as an arrow, and may have coloring to emphasise personal compatibility or lack thereof (e.g., a red to blue gradient).

With reference toFIG. 1, the DNA extraction and probing24can be performed by the following process. The laboratory receives a DNA sample20from the interested individual or the respective matchmaker. DNA is extracted from the swab containing the DNA sample20. An extraction kit, such as Life Technologies PureLink Genomic DNA kits (Catalogue number: K1820-02) can be used. If extraction fails, then another swab, if available, can be processed.

To amplify the HLA genes of interest, which are discussed above, two 20 microliter (ul) PCRs can be run on the extracted DNA. The same settings may be used for both reactions, so that the reactions can be run at the same time. The following materials may be used: Taq DNA polymerase (recombinant; catalogue number: 10342-020) available from Invitrogen by Life Technologies, 10 mM dNTP mix (PCR grade; catalogue number: 18427-088) available from Invitrogen by Life Technologies, and primers to target the HLA genes of interest, available from Sigma-Aldrich and diluted to a stock concentration of 100 uM and a working stock of 10 uM. Stocks of the PCR buffer are then made (4 or 5 aliquots, stored at −20 C) as follows: 10×PCR buffer (100 ul), dNTP mixture (20 ul), MgCl2 (30 ul), and Millipore H2O (725 ul). Two PCR reactions may then be set up, each as follows: stock of PCR buffer mixture (17.5 ul), primer mix (1 ul), DNA (1 ul), and Taq (0.5 ul). The PCR reactions may then be run with the following settings, shown in Table 4.

To determine which allele groups are present in the DNA sample, two 20 ul quantitative real-time (qRT) PCR reactions using SYBR green a well plate with embedded primers for the allele groups, as discussed above. The well plate can be primed with multiple instances of each primer, so that one well plate can be used for multiple samples, such as samples from two interested persons whose compatibility is being determined. When the matchmaker (or interested person) sends to the laboratory samples from two interested persons in the same package, use of the well plate in this manner can help reduce the chance that results will be mixed up. A 96-well plate can be used, such as the type available from Bio-Rad. Additional materials include: iQSYBR Green Supermix (catalogue number: 170-8885) and DNA from both PCR reactions and from genomic extracted DNA. The PCR products can be diluted 1:20 before being added to the qRT reaction. Per reaction, the following volumes shown in Table 5 may be used.

The qRT-PCR reactions may then be run with the following settings, shown in Table 6.

It will be understood by one of ordinary skill in the art upon reading this disclosure that any of the volumes, temperature, times, and other quantities discussed herein can be modified to accommodate different yet equivalent protocols. Moreover, suitable substitutes for specified equipment and material will also be apparent to such person in light of this disclosure, and the specified equipment and material is not intended to be limiting.

With reference toFIG. 12a PCR primer plate170is shown. The primer plate170is an example of a suitable PCR primer array. The primer plate170has 96 wells divided into two regions172,174. The first region172contains primers for analysis of a first interested person's HLA genes in 45 wells. The second region174contains primers for analysis of a second interested person's HLA genes in 45 wells. The primer sets may be assigned based on Table 3, above, with one well of each region172,174containing the primers listed in one row of the table.

Other primer array configurations are also contemplated. For example, a 96-well primer plate can be used for 16 groupings of six primer sets assigned based on Table 1, above. This can allow batched analysis for 16 interested individuals, such as eight potential couples. In another example, a 384-well primer plate is used.

The PCR primer arrays discussed herein, such as the primer plates170,180, are not limited by geometry or materials and can have a rectangular shape, a disc shape, a linear strip of tubes, a collection of loose tubes, or other structure. In addition, the wells need not be limited to any particular structure or layout, and the figures merely depict examples.

The allele groups discussed herein are examples. More or fewer groups can be used and group definitions can be modified, without departing from the scope of the invention. Sequences can be obtained from an IMGT/HLA Database (http://www.ebi.ac.uk/ipd/imgt/hla/align.html), with exons 2 and 3 being suitable for HLA-A and HLA-B and exon2being suitable for HLA-DRB1. It may be desirable to select these particular regions because they are some of the more diverse regions of the HLA genes, and further are relatively small and amenable to quantitative real-time polymerase chain reaction (qRT-PCR) processing. Sequences can be formatted and aligned using a tool such as the Mafft multiple sequence alignment program (http://mafft.cbrc.jp/alignment/server/index.html). Phylogenetic analysis of the sequences, including bootstrapping of the alignment to increase the robustness of the phylogenetic tree, can be performed using a program such as Clustal X2 (http://www.clustal.org/clustal2/). Phylogenetic trees, such as the condensed trees discussed herein, can be created using a tool such as NJplot (http://pbil.univ-lyonl.fr/software/njplot.html), and serological groups may be referenced. Finally, alleles can be segregated into groups using Jalview (http://www.jalview.org/), which can aid visualization of sequences for primer design.

An alpha test was performed using a sample size of 65 individuals. Approximately half of the 65 individuals (15 couples) reported to be in relationships, while the remainder identified as single. Using the teachings disclosed herein, the similarity of HLA alleles between couples and singles randomly assorted into theoretical couples was determined. As illustrated inFIG. 14, normalized results showed that randomly matched pairs had a 2-fold greater similarity in their HLA genes than actual dating couples.

Advantages of the above techniques for matchmakers or third-party services, such as dating companies, may include higher success rates, more satisfied customers, increased clientele, and a competitive advantage. Advantages of the above techniques for individuals may include increased sense of physical attraction with one's partner, a more satisfying sex life, healthier children, increased fertility rates, and a higher likelihood of long-term relationship.

While the foregoing provides certain non-limiting example embodiments, it should be understood that combinations, subsets, and variations of the foregoing are contemplated. The monopoly sought is defined by the claims.