My Master's defense recapping the SNP assessment and sockeye senescence projects I worked on during my tenure as a grad student at the University of Washington.
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...
Genetic and phenotypic variation in sockeye salmon
1. Genetic and phenotypic diversity in
sockeye salmon, Oncorhynchus nerka
Caroline Storer
University of Washington
School of Aquatic and Fishery Sciences
Committee:
Thomas Quinn
Steven Roberts (Co-chair)
James Seeb (Co-chair)
William Templin
1
2. Outline
• Introduction
– Sockeye salmon
• Chapter 1:
– Evaluating the performance of SNPs for individual
assignment
• Chapter 2:
– Characterizing differences in gene expression
patterns associated with variability in senescence
2
16. Molecular Markers, Today
• Single nucleotide polymorphisms (SNPs)
- Abundant
ACTCG - The number of available
markers is growing
- Methods are robust and
ACACG automated
- Not all SNPs are equal
SNP locus
16
18. Chapter 1: Objectives
• Develop new SNP markers for sockeye salmon
• Rank all SNPs in sockeye salmon based on
performance
18
19. Chapter 1: Objectives
• Develop new SNP markers for sockeye salmon
• Rank all SNPs in sockeye salmon based on
performance
• Evaluate the success of different ranking
methods
19
20. Measuring Genetic Variation
South-central
Bristol Bay Alaska
Russia British
Alaska Peninsula
Columbia
Washington
Genotyped 12 populations, 61- 93 fish per population,
using 114 SNPs 20
21. Measuring Genetic Variation
Bristol Bay
Alaska
Principal Coordinate 2 (15.5%)
Peninsula
Washington
South-central Alaska
British
Columbia
Russia
Principal Coordinate 1 (44.5%)
21
22. Bristol Bay
Alaska Peninsula
South-central Alaska
Russia
British Columbia Washington
22
23. SNP Ranking
• Performed using only half of available
individuals
– Remaining individuals reserved for panel testing
23
24. SNP Ranking
• Performed using only half of available
individuals
– Remaining individuals reserved for panel testing
• Each SNP ranked by 5 measures
24
25. SNP Ranking
• FST
- SNPs ranked by ability to measure population variance
25
26. SNP Ranking
• FST
- SNPs ranked by ability to measure population variance
• Informativness (In)
- Potential for a genotype to belong to specific population versus a
population average
26
27. SNP Ranking
• FST
- SNPs ranked by ability to measure population variance
• Informativness (In)
- Potential for a genotype to belong to specific population versus a
population average
• Locus contribution (LC)
- Average contribution of each SNP to principal components
27
28. SNP Ranking
• FST
- SNPs ranked by ability to measure population variance
• Informativness (In)
- Potential for a genotype to belong to specific population versus a
population average
• Locus contribution (LC)
- Average contribution of each SNP to principal components
• BELS
- SNPs ranked by reduction in performance when removed
28
29. SNP Ranking
• FST
- SNPs ranked by ability to measure population variance
• Informativness (In)
- Potential for a genotype to belong to specific population versus a
population average
• Locus contribution (LC)
- Average contribution of each SNP to principal components
• BELS
- SNPs ranked by reduction in performance when removed
• WHICHLOCI
- Algorithm for ranking SNPs based on power for individual assignment
29
30. SNP Ranking
1
21
Average SNP rank
41
61
81
top ranked SNPs
101
0 10 20 30 40 50 60 70 80 90 100 110
30
SNPs ordered by average rank
31. Panel Design
• Created 48- and 96-SNP panels containing top
ranked SNPs
– for each of the five ranking measures
– for average SNP rank
– for randomly selected SNPs
31
42. Panel Testing
• 2 panel testing methods
– Empirical
• Remaining individuals assigned to a baseline of
individuals used for SNP ranking
• Assignment tests performed in ONCOR
42
43. Panel Testing
• 2 panel testing methods
– Empirical
• Remaining individuals assigned to a baseline of
individuals used for SNP ranking
• Assignment tests performed in ONCOR
– Simulated
• 1000 individuals simulated using population allele
frequencies from remaining individuals
• Assignment tests replicated 500 times
43
44. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
44
45. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
45
46. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
46
47. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
47
48. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
48
49. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
49
50. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
50
51. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
51
52. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
52
53. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
53
54. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
54
55. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
55
56. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
56
57. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
57
58. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
58
59. Panel Testing – Empirical data
1.0
Probability of correct assignment
0.8
0.6
0.4
0.2
0.0
59
60. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
60
61. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
61
62. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
62
63. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
63
64. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
64
65. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
65
66. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
66
67. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
67
68. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
68
69. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
69
70. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
70
71. Panel Testing – Simulated data
1.0
Probability of correct assignment
0.9
0.8
0.7
71
73. Findings
• Greater variation and lower panel performance using
empirical data
• In general, 96-SNP panels performed better
73
74. Findings
• Greater variation and lower panel performance using
empirical data
• In general, 96-SNP panels performed better
• FST, In, and LC panels had the highest average
probability of correct assignment
74
75. Findings
• Greater variation and lower panel performance using
empirical data
• In general, 96-SNP panels performed better
• FST, In, and LC panels had the highest average
probability of correct assignment
• Random SNP selection preforms nearly as well as
ranking when all available SNPs are used
75
76. Findings
• Greater variation and lower panel performance using
empirical data
• In general, 96-SNP panels performed better
• FST, In, and LC panels had the highest average
probability of correct assignment
• Random SNP selection preforms nearly as well as
ranking when all available SNPs are used
• BELS panels had the lowest average probability of
correct assignment
76
79. Conclusions
• Common ranking methods perform differently
• More SNPs is often better
• When choosing a small proportion of available SNPs
the ranking approach is more important
79
80. Conclusions
• Common ranking methods perform differently
• More SNPs is often better
• When choosing a small proportion of available SNPs
the ranking approach is more important
• Empirical panel tests performance on real (vs.
simulated) populations
80
81. Conclusions
• Common ranking methods perform differently
• More SNPs is often better
• When choosing a small proportion of available SNPs
the ranking approach is more important
• Empirical panel tests performance on real (vs.
simulated) populations
• Simulated data highlights performance based on
SNP composition
81
83. Implications
• 43 new SNPs are now available for sockeye salmon
- Already in use
40
Stock
Togiak
Catch (millions of sockeye salmon)
Igushik
Wood
30
Nushagak
Kvichak
Alagnak
Naknek
20
Egegik
Ugashik
10
0
1960 1970 1980 1990 2000 2010
Year
83
85. Implications
• 43 new SNPs are now available for sockeye salmon
- Already in use
• Methods outlined are important for developing
SNP panels for any system or question
85
92. Salmon Senescence
• Undergo rapid senescence
• Rates of senescence vary:
– in the same populations (Perrin & Irvine 1990)
– between populations (Carlson et al. 2007)
• Characterized by physiological trade-offs
92
93. Salmon Senescence
• Characterized by physiological trade-offs
• Increased energetic investment in
reproduction
• Starvation and stress
93
Finch 1994; Gotz et al. 2005; Maldonado et al. 2002
94. Salmon Senescence
• Characterized by physiological trade-offs
• Increased energetic investment in
reproduction
• Starvation and stress
• Decreased immune function
• Increased oxidative stress
• Central nervous system disintegration
94
Finch 1994; Gotz et al. 2005; Maldonado et al. 2002
95. Objectives
• Uncover driving mechanisms of senescence
– Develop quantitative gene expression assays for
genes associated with aging
– Characterize senescent specific expression
patterns in sockeye salmon
95
100. NMDA
• Involved in synaptic plasticity
and memory
• Linked to neurodegenerative
disorders
100
101. NMDA
• Involved in synaptic plasticity P = 0.12
and memory 50
50
• Linked to neurodegenerative 40
40
disorders
Gene expression
30
• No significant difference
30
20
20
10
10
0
0
Pre- Senescent
senescent
101
102. OMP1
• Olfactory marker proteins
(OMP) necessary for the
function of olfactory
receptor neurons
102
103. OMP1
• Olfactory marker proteins
(OMP) necessary for the P = 0.32
function of olfactory 150
150
receptor neurons
• No significant difference
Gene expression
100
100
50
50
0
0
Pre- Senescent
senescent
103
104. GnRHp
• Part of the GnRH axis which
plays a critical role in
reproduction
104
105. GnRHp
• Part of the GnRH axis which
plays a critical role in 1000
1000
P = 0.15
reproduction
800
800
• No significant difference
Gene expression
600
600
400
400
200
200
0
0
Pre- Senescent
senescent
105
118. Findings
• Greater expression in senescent salmon
• Greater variation in expression of senescent
salmon
• Significant differences detected for two genes:
TERT (aging) and Viperin (immune function)
118
120. Conclusions
• Strong response detected in immune function
– Driving mechanism or associated process?
• Telomerase activity represents senescence
specific signal
120
122. Implications
• New assays can be used at any stage of the
sockeye salmon life cycle
• Telomere dynamics important for
understanding variation in rates of senescence
122
125. Telomere Dynamics
Population 1 Population 2
• Fast senescence • Slow senescence
• Low telomerase • High telomerase
expression expression
125
126. Implications
• New assays can be used at any stage of the
sockeye salmon life cycle
• Telomere dynamics important for
understanding variation in rates of senescence
126
127. Implications
• New assays can be used at any stage of the
sockeye salmon life cycle
• Telomere dynamics important for
understanding variation in rates of senescence
– Measure of life history diversity (rate of
senescence)
127
129. Acknowledgments
Roberts Lab: Funding:
• Sam White • Alaska Sustainable Salmon Fund
• Steven Roberts • Bristol Bay Regional Seafood
• Emma Timmins-Schiffman Development Group
• Dave Metzger • The Gordon and Betty Moore
• Mackenzie Gavery Foundation
• The School of Aquatic and Fishery
Seeb Lab: Sciences
• Jim Seeb • OACIS NSF GK12
• Lisa Seeb
• Carita Pascal Committee:
• Eleni Petrou • Thomas Quinn
• Meredith Everett • Steven Roberts (Co-chair)
• Wes Larson • James Seeb (Co-chair)
• Marissa Jones • William Templin
• Sewall Young
• Ryan Waples FRIENDS and FAMILY Cohort ‘09
129
Genetic information greatly contributes to the management of Alaska's fishery resources. Along with other kinds of information, genetic markers are used to identify appropriate population units (discrete stocks) for management. These markers can also be used to identify individuals of particular stocks in mixed-stock fisheries to allow escapement of spawners to declining populations. The ability to identify stock origins can also assist the enforcement of conservation closures. In addition to providing population tags, genetic variability itself is important for the survival of a population. The State's genetic policy attempts to project the level and integrity of genetic variability within populations, by limiting stock transfers between distinct stocks and by limiting the effects of hatchery fish on wild stocks.
Genetic information greatly contributes to the management of Alaska's fishery resources. Along with other kinds of information, genetic markers are used to identify appropriate population units (discrete stocks) for management. These markers can also be used to identify individuals of particular stocks in mixed-stock fisheries to allow escapement of spawners to declining populations. The ability to identify stock origins can also assist the enforcement of conservation closures. In addition to providing population tags, genetic variability itself is important for the survival of a population. The State's genetic policy attempts to project the level and integrity of genetic variability within populations, by limiting stock transfers between distinct stocks and by limiting the effects of hatchery fish on wild stocks.
Genetic information greatly contributes to the management of Alaska's fishery resources. Along with other kinds of information, genetic markers are used to identify appropriate population units (discrete stocks) for management. These markers can also be used to identify individuals of particular stocks in mixed-stock fisheries to allow escapement of spawners to declining populations. The ability to identify stock origins can also assist the enforcement of conservation closures. In addition to providing population tags, genetic variability itself is important for the survival of a population. The State's genetic policy attempts to project the level and integrity of genetic variability within populations, by limiting stock transfers between distinct stocks and by limiting the effects of hatchery fish on wild stocks.
Mention allele
Nucleotide represents an allele
FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components. BELS and WHICHLOCI provide each locus a rank based on the accuracy of individual assignment for that locus and the value lost when the locus is removed from the panel in a jackknife fashion. Loci that result in the greatest loss in individual assignment performance when removed receive the highest score.
FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components. BELS and WHICHLOCI provide each locus a rank based on the accuracy of individual assignment for that locus and the value lost when the locus is removed from the panel in a jackknife fashion. Loci that result in the greatest loss in individual assignment performance when removed receive the highest score.
FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components.
FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components.
FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components.
FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components.
FST, LC, and In are all measures of genetic diversity based on differences in allele frequencies observed at a locus, while BELS and WHICHLOCI are scores based on maximizing the likelihood of assigning a genotype to the correct population. FST and LC are essentially measures of genetic diversity. Informativeness (In) has been shown to be correlated with FST by Rosenberg et al. (2003). Informativness’s relationship to LC was determined using a Spearman’s rank correlation. The LC was determined using a multivariate locus comparison method developed by Moazami-Goudarzi and Laloë (2002) and implemented in S-Plus (MathSoft, Inc, 2000). Here, locus contribution was determined for the first five principal components.
Up to 13 loci differed between panelsFST and In panels shared all but 1 locus
Up to 13 loci differed between panelsFST and In panels shared all but 1 locus
Up to 13 loci differed between panelsFST and In panels shared all but 1 locus
Up to 13 loci differed between panelsFST and In panels shared all but 1 locus
FST, In, and LC were most similar with only 3 - 7 different loci16 loci differed between these 3 panels and the WHICHLOCI panelBELS panel shared only 12-20 loci with another panel
FST, In, and LC were most similar with only 3 - 7 different loci16 loci differed between these 3 panels and the WHICHLOCI panelBELS panel shared only 12-20 loci with another panel
FST, In, and LC were most similar with only 3 - 7 different loci16 loci differed between these 3 panels and the WHICHLOCI panelBELS panel shared only 12-20 loci with another panel
FST, In, and LC were most similar with only 3 - 7 different loci16 loci differed between these 3 panels and the WHICHLOCI panelBELS panel shared only 12-20 loci with another panel
FST, In, and LC were most similar with only 3 - 7 different loci16 loci differed between these 3 panels and the WHICHLOCI panelBELS panel shared only 12-20 loci with another panel
The probability of correct assignment from ONCOR is the probability that individual of unknown origin belongs to a given population in the baseline. A very simplified explanation of how the probability is calculated is that it uses the frequency of an unknown fish's genotype in a baseline pop and estimated mixture proportions (which is a conditional maximum likelihood estimate).
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
BELS is the poorest performing, in fact it is the only 96-SNP panel that does not perform better than all 48-SNP panels
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
In the empirical data there was greater variation in probability of correct assignment and fewer significant differences between panel performances samples may be of poor tissue quality, be missing genotypes, or might not have diagnostic genotypes, making them difficult to assign back to populations of origin
demonstrate how common locus-ranking methods perform differently when developing a SNP panelThe steps outlined here provide a starting place for developing a minimum panel for maximum assignment for any specific system or question
demonstrate how common locus-ranking methods perform differently when developing a SNP panelThe steps outlined here provide a starting place for developing a minimum panel for maximum assignment for any specific system or question
demonstrate how common locus-ranking methods perform differently when developing a SNP panelThe steps outlined here provide a starting place for developing a minimum panel for maximum assignment for any specific system or question
demonstrate how common locus-ranking methods perform differently when developing a SNP panelThe steps outlined here provide a starting place for developing a minimum panel for maximum assignment for any specific system or question
decreased immune function, increased oxidative stressdamage is occurring in all major organ systems including the central nervous system where neurons and neurites begin to disintegrate
decreased immune function, increased oxidative stressdamage is occurring in all major organ systems including the central nervous system where neurons and neurites begin to disintegrate
decreased immune function, increased oxidative stressdamage is occurring in all major organ systems including the central nervous system where neurons and neurites begin to disintegrate
decreased immune function, increased oxidative stressdamage is occurring in all major organ systems including the central nervous system where neurons and neurites begin to disintegrate
decreased immune function, increased oxidative stressdamage is occurring in all major organ systems including the central nervous system where neurons and neurites begin to disintegrate
public sequence archives from Genebank.
public sequence archives from Genebank.
olfactory receptor neurons (ORN) to transmit signals of specific odors of amino acids and bile salts to their brain that sockeye salmon displaced from their spawning grounds within Hansen Creek returned to their breeding site
olfactory receptor neurons (ORN) to transmit signals of specific odors of amino acids and bile salts to their brain that sockeye salmon displaced from their spawning grounds within Hansen Creek returned to their breeding site
