Attribute sieving and profiling with sample enrichment by optimized pooling

A method of selecting biological samples, from a set of candidate samples, in accordance with a pattern of desired attributes wherein sample pools of different samples are formed and each attribute in the attribute pattern can be determined by a method capable of detecting, for a particular pool of samples, either the presence of desired attributes in at least one of the samples, but not all of the samples in that pool, or the presence or absence of the desired attributes in all samples in that pool, the process comprising:

• determining, based on the desired attributes, the quantity “d” which is the preferred number of samples per pool by selecting as “d” a positive integer value which, based on the expected frequencies of the desired attributes in the population from which samples are drawn, produces the maximum or a value near the maximum of the expected number of samples in pools whose constituent samples all have, or all do not have, the desired attributes;
• dividing the plurality of samples into subsets, comprising approximately “d” samples per subset such that each of the plurality of samples is included in at least one subset, and, forming unique pools with aliquots from different sample subsets;
• forming, for each pool, attribute-specific, source-tagged reaction products each having a source tag identifying the pool and a marker-tag identifying a particular desired attribute;
• identifying unambiguous pools wherein samples in said unambiguous pools all have, or all do not have, the desired attributes, or identifying ambiguous pools wherein at least one constituent sample, but not all constituent samples, does not have the desired attributes;
• identifying and selecting samples from unambiguous pools for the desired attributes;
• optionally either leaving additional attributes in their attribute profiles undetermined, or further analyzing the selected samples;
• and
• optionally further analyzing the constituent samples from ambiguous pools to resolve which have and which do not have the desired attributes in the pattern.

A method of enriching a sample set for desired attributes by identifying samples in pools of samples that are ambiguous for said attributes;

• the process comprising pooling of different biological samples and wherein each of said attributes can be determined by a method capable of detecting, for a particular pool of samples, either the presence of the desired attributes in at least one of the samples in that pool, or the presence or absence of the desired attributes in all samples in that pool, the process comprising:
• determining, based on all the attributes, the quantity “d” which is the preferred number of samples per pool by selecting a positive integer value as “d” which produces the maximum or a value near the maximum of the product of:
• d times the number of expected sample pools whose constituent samples all have, or do not have, the desired attributes, based on the expected frequencies of the desired attributes in the population from which samples are drawn;
• dividing the plurality of samples into subsets, comprising approximately d samples per subset such that each of the plurality of samples is included in at least one subset, and, forming unique pools with aliquots from different sample subsets;
• forming, for each pool, attribute-specific, source-tagged reaction products each having a source tag identifying the pool and a marker-tag identifying a particular desired attribute;
• identifying ambiguous pools wherein at least one, but not all samples in said ambiguous pools have the desired attributes;
• and
• identifying and selecting samples from the ambiguous pools for further analysis.