PRECISE SIMULATION OF PROGENY DERIVED FROM RECOMBINING PARENTS

• US 20140136166A1
• Filed: 09/17/2013
• Published: 05/15/2014
• Est. Priority Date: 11/13/2012
• Status: Abandoned Application

• Current Assignee: GlobalFoundries, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: HAIMINEN, Niina S., PARIDA, Laxmi P., UTRO, Filippo
• Application Number:
  US14/029,265
• Publication Number:
  US 20140136166A1
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/20 Probabilistic models

Abstract

Various embodiments simulate crossover events on a chromosome. In one embodiment, a number Y of positions to be selected on a simulated chromosome is determined. Y positions j₁, . . . , j_y on the simulated chromosome are selected. A crossover event is placed at one or more of the positions j₁, . . . , j_y based on Y>0. An additional number Y′ of positions j′₁, . . . , j′_y to be selected on the simulated chromosome is determined. Y′ additional positions j′₁, . . . , j′_y on the simulated chromosome are selected. An additional crossover event is placed at one or more of the additional positions j′₁, . . . , j′_y based on Y′>0 and a neighborhood t associated with the one or more of the additional positions j′₁, . . . , j′_y being free of crossover events. A set of crossover event locations is identified based on the one or more of the positions j₁, . . . , j_y and additional positions j′₁, . . . , j′_y at which a crossover event has been placed.

Claims

1. An information processing system for simulating crossover events on a chromosome, the information processing system comprising:

a memory;

a processor communicatively coupled to the memory; and

a progeny simulation module communicatively coupled to the memory and the processor, wherein the progeny simulation module is configured to perform a method comprising;

determining, by a processor, a number Y of positions to be selected on a simulated chromosome, wherein the simulated chromosome has a genetic length L with a crossover rate of p;

selecting, based on the determining, Y positions j₁, . . . , j_y on the simulated chromosome;

placing a crossover event at one or more of the positions j₁, . . . , j_y that have been selected based on Y being greater than 0;

determining an additional number Y′

of positions j′

₁, . . . , j′

_y to be selected on the simulated chromosome;

selecting, based on the determining, Y′

additional positions j′

₁, . . . , j′

_y on the simulated chromosome;

placing an additional crossover event at one or more of the additional positions j′

₁, . . . , j′

_y that have been selected based on Y′

being greater than 0 and a neighborhood t associated with the one or more of the additional positions j′

₁, . . . , j′

_y being free of crossover events; and

identifying a set of crossover event locations on the simulated chromosome based on the one or more of the positions j₁, . . . , j_y and the one or more of the additional positions j′

₁, . .. , j′

_y at which a crossover event has been placed.

2. The information processing system of claim 1, wherein the method further comprises:

determining, for at least a first of the positions j₁, . . . j_Y at which a crossover event has been placed, if at least one crossover event is located at a position on the simulated chromosome within a t neighborhood of the first of the positions j₁, . . . j_Y, wherein t=X_c, where X_c is a random variable drawn from a uniform discrete distribution on [m,n] where m<

n, where c=(m+n)/2; and

removing the crossover event placed at the first of the positions j₁, . . . j_Y with a probability q=(1−

2p) based on the at least one crossover event being located at the position on the simulated chromosome within the t neighborhood. determining, for at least a first of the positions j₁, . . . , j_y at which a crossover event has been placed, if at least one crossover event is located at a position on the simulated chromosome within a t neighborhood of the first of the positions j₁, . . . , j_y, wherein t=X_c, where X_c is a random variable drawn from a uniform discrete distribution on [m,n] where m<

n, where c=(m+n)/2; and

removing the crossover event placed at the first of the positions j₁, . . . , j_y with a probability q=(1−

2p) based on the at least one crossover event being located at the position on the simulated chromosome within the t neighborhood.

3. The information processing system of claim 1, wherein the method further comprises

determining, for at least a first of the additional positions j′

₁, . . . , j′

_y at which a crossover event has been placed, if at least one crossover event is located at a position on the simulated chromosome within a t neighborhood of the first of the additional positions j′

₁, . . . , j′

_y, wherein t=X_c, where X_c is a random variable drawn from a uniform discrete distribution on [m,n] where m<

n, where c=(m+n)/2; and

removing the crossover event placed at the first of the additional positions j′

₁, . . . , j′

_y with a probability q=(1−

2p) based on the at least one crossover event being located at the position on the simulated chromosome within the t neighborhood.

4. The information processing system of claim 1, wherein the number Y of positions j₁, . . . , j_y are selected from a Poisson distribution with a mean λ

=pL, where p=0.01, wherein the number Y′

of positions j′

₁, . . . , j′

_y are selected from a Poisson distribution with a mean λ

′

=p′

L, and

5. The information processing system of claim 1, wherein the genetic length L comprises a plurality of segment lengths Z₁, Z₂, . . . , Z_L (Z_l>

0), and wherein each segment length Z₁, Z₂, . . . , Z_L has a corresponding crossover rate p₁, p₂, . . . , p_L (0≦

p_l<

1, l=1, . . . , L), and wherein the set of crossover event locations is a concatenation of crossover positions placed on the simulated chromosome for each segment length Z₁, Z₂, . . . , Z_L based on each of the corresponding crossover rates p₁, p₂, . . . , p_L.

6. A non-transitory computer program product for simulating crossover events on a chromosome, the non-transitory computer program product comprising:

a storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising;

determining, by a processor, a number Y of positions to be selected on a simulated chromosome, wherein the simulated chromosome has a genetic length L with a crossover rate of p;

selecting, based on the determining, Y positions j₁, . . . , j_y on the simulated chromosome;

placing a crossover event at one or more of the positions j₁, . . . , j_y that have been selected based on Y being greater than 0;

determining an additional number Y′

of positions j′

₁, . . . , j′

_y to be selected on the simulated chromosome;

selecting, based on the determining, Y′

additional positions j′

₁, . . . , j′

_y on the simulated chromosome;

placing an additional crossover event at one or more of the additional positions j′

₁, . . . , j′

_y that have been selected based on Y′

being greater than 0 and a neighborhood t associated with the one or more of the additional positions j′

₁, . . . , j′

_y being free of crossover events; and

identifying a set of crossover event locations on the simulated chromosome based on the one or more of the positions j₁, . . . , j_y and the one or more of the additional positions j′

₁, . . . , j′

_y at which a crossover event has been placed.

7. The non-transitory computer program product of claim 6, wherein the method further comprises:

determining, for at least a first of the positions j₁, . . . , j_y at which a crossover event has been placed, if at least one crossover event is located at a position on the simulated chromosome within a t neighborhood of the first of the positions j₁, . . . , j_y, wherein t=X_c, where X_c is a random variable drawn from a uniform discrete distribution on [m,n] where m<

n, where c=(m+n)/2; and

removing the crossover event placed at the first of the positions j₁, . . . , j_y with a probability q=(1−

2p) based on the at least one crossover event being located at the position on the simulated chromosome within the t neighborhood.

8. The non-transitory computer program product of claim 6, wherein the method further comprises:

determining, for at least a first of the additional positions j′

₁, . . . , j′

_y at which a crossover event has been placed, if at least one crossover event is located at a position on the simulated chromosome within a t neighborhood of the first of the additional positions j′

₁, . . . , j′

_y, wherein t=X_c, where X_c is a random variable drawn from a uniform discrete distribution on [m,n] where m<

n, where c=(m+n)/2; and

removing the crossover event placed at the first of the additional positions j′

₁, . . . , j′

_y with a probability q=(1−

2p) based on the at least one crossover event being located at the position on the simulated chromosome within the t neighborhood.

9. The non-transitory computer program product of claim 6, wherein the number Y of positions j₁, . . . , j_y are selected from a Poisson distribution with a mean λ

=pL, where p=0.01.

10. The non-transitory computer program product of claim 9, wherein the number Y′

of positions j′

₁, . . . , j′

_y are selected from a Poisson distribution with a mean λ

′

=p′

L, and

11. The non-transitory computer program product of claim 6, wherein the genetic length L comprises a plurality of segment lengths Z₁, Z₂, . . . , Z_L (Z_l>

0), and wherein each segment length Z₁, Z₂, . . . , Z_L has a corresponding crossover rate p₁, p₂, . . . , p_{L (}0≦

p_l<

1, l=1, . . . , L), and wherein the set of crossover event locations is a concatenation of crossover positions placed on the simulated chromosome for each segment length Z₁, Z₂, . . . , Z_L based on each of the corresponding crossover rates p₁, p₂, . . . , p_L.