Evolutionary arguments in medical genomics

International Life Sciences Workshop
“Decision-Making in Biomedical Science – Meet Experts”
September 12 – 16 | 2014
Potsdam | Germany
Evolutionary arguments in medical genomics
Nikita N. Khromov-Borisov
Pavlov First Saint Petersburg State Medical University
Saint Petersburg, Russia
Nikita.KhromovBorisov@gmail.com
+7 952-204-89-49; +7 921-449-29-05
http://independent.academia.edu/NikitaKhromovBorisov
https://www.researchgate.net/profile/Nikita_Khromov-Borisov?ev=hdr_xprf
1

Slides are freely available to all
Department of Physics, Mathematics and Informatics
Pavlov First Saint Petersburg State Medical University
+7-952-204-89-49; +7-921-449-29-05
2

Key words:
• genetics of predisposition,
• genetic polymorphism,
• genetic association,
• evolutionary medical genomics,
• neutral evolution,
• genetic load,
• opposite pleiotropy,
• homeostasis,
• reproducibility,
• predictive values,
• Bayesian graphs,
3

Albert Einstein
• ‘‘We can’t solve problems by using
• the same kind of thinking we used when we created
them’’
• Cited by: Heng H.H.Q. The genome-centric concept:
resynthesis of evolutionary theory. BioEssays, 2009;
31: 512–525.
4

Methodology of restrictions and limitations
• Most fundamental scientific principles are in fact exclusions
(“taboos”) and the progress of science is associated with the
recognition of the importance of some principal restrictions
and/or limitations.
• It is impossible to create perpetuum mobile.
• It is impossible to move with the superluminal speed.
• It is impossible to heat the hot body by the cold one.
• Two identical fermions (e.g. two electrons) cannot occupy
the same quantum state simultaneously (Pauli exclusion
principle).
• Replication, transcription and/or translation of proteins is
impossible (Central dogma of molecular biology). Etc.
5

Science is not omnipotent
• Not always and not all of the results of basic
research lead immediately to practical application.
• Some of them only indicate the insurmountable
uncertainty and fundamental limitations of our
abilities.
• In particular, we are talking about trying to
diagnose a predisposition to multifactorial diseases
and syndromes, as well as susceptibility to certain
activities (e.g., to sport achievements or to specific
profession), with the help of genetic testing.
6

Post genomic era
• “We bought at the price of a dollar for
• letter huge book without a table of
contents.”
• Eric Lander
• After sequencing the human genome we
found ourselves in a position of the player of
Russian TV capital-show “Field of Dreams”,
who has guessed all the letters, but was
unable to read the word.
7

• In contrast to rare Mendelian diseases, extensive family-based
linkage analysis in the 1990s was largely unsuccessful
in uncovering the basis of common diseases that afflict most
of the population.
• These diseases are polygenic, and there were no systematic
methods for identifying underlying genes.
• As of 2000, only about a dozen genetic variants (outside the
HLA locus) had been reproducibly associated with common
disorders.
• A decade later, more than 1,100 loci affecting more than 165
diseases and traits have been associated with common traits
and diseases, nearly all since 2007.
8

Genetics of predispositions
• Popular genetic association studies (GAS),
that is studies of genetic susceptibility to
disease or ability to any particular type of
activity (e.g., to high achievements in sport)
is appropriate to call the genetics of
predispositions.
9

Genetics of predispositions
• Perhaps the most fundamental result of the genetics of
predispositions consists in contradictory results and their
contradictory interpretations.
• The main reason for the contradictory results and
contradictory interpretations is their poor reproducibility
and extremely low predictive ability.
• Therefore we should not trust the assertions about the
alleged practical (clinical) value of such countless studies.
• They are unfounded, if not confirmed repeatedly in
independent studies.
• But even if they are reproduced, their practical (clinical)
usefulness should be demonstrated.
10

Sources of uncertainty of
genetic chiromancy and
predictions
11

Sample sizes in physics, chemistry, biology and
medicine
• Physicists and chemists works with the samples of different
substances which contain 6∙1023 of particles (atoms or
molecules) in 1 mole of the pure substance.
• Even 1 nanomole of given substance contains about 1014
such particles.
• These particles may be regarded as rather identical.
• However, we need not to forget that even on the atomic
level there are several isotopes of a given chemical element.
• And some of them are radioactive.
• In medicine researchers are limited with the size of the
world population which is less then 1010 (7.26∙109) .
• And human populations are very heterogeneous.
12

Principal contradiction
• Almost all people are dissimilar, even monozygotic
(“identical”) twins (CNV, immunoglobulins, fingerprints ).
• Surely this fact is one of the main sources of the low
reproducibility and predictive ability of the results in
biomedicine.
• Thus, the genetic uniqueness of each person comes into
contradiction with the statistical methodology, which
requires to analyze large amounts (hundreds and thousands)
of identical persons to achieve the certain conclusions.
13

• Thanks to the genome sequence projects, the
number of genes in the human has been calculated
to be a modest 31,897 (http://eugenes.org/), a
relatively small number when one considers that
yeast has 7,547, thale cress (Arabidopsis thaliana)
contains 29,388 ORFs (open reading frames), and a
measly little worm (Caenorhabditis elegans) has
23,399.
• Indeed, Mus musculus (the mouse) has 6,000 genes
more than humans, and one may wonder if we
really are that complex after all!
14

Variety of determinants of complex phenotype
exemplified by the heart hypertrophy
Marian A.J. Molecular genetic studies of complex phenotypes. Translational Research, 2012; 159: 64–79
15

A great variety of genetic polymorphism
• SNP — single nucleotide polymorphism,
• miRNA — RNA interference and micro-RNA,
• piRNA — piwi-interacting RNA,
• lncRNA — long non-coding RNA,
• tmRNA — transfer-messenger RNA,
• eccDNA — extrachromosomal circular DNA,
• microDNA — short eccDNA,
• CNV — copy number variations.
• It seems that the association of the last 7 newly discovered elements
with diseases is stronger than for SNP.
• They differ even in monozygotic twins and can vary between individual
cells of the same tissue (e.g., in the neurons of the brain).
• Let’s don’t forget also about the individual variation in the
immunoglobulin genes.
16

The number of allele (DNA-sequence variation)
combinations is rather incalculable
• The number of DNA sequence variations (alleles) in human genome is
astronomical.
• According to the NBCI dbSNP build 141 on May 21, 2014 there are
43,737,321 SNPs (single nucleotide polymorphisms).
• The number of their combinations can not be counted; obviously, it is
much more than the number of people on Earth - 7.26 billion and
perhaps even more than the number of atoms in the Universe - 1067.
• Therefore, it is principally impossible to prove that given unique
genotype predisposes to given disease or to certain propensity.
• For this we need to have a large sample of subjects with given genotype,
but it is unique.
• For instance, in forensic genetics using 15 loci with about 10 different
number of STRs in each is sufficient to identify any unrelated person.
17

Situation when genetic uniqueness is
practically useful: Genetic passport
18

Theodosius Dobzhansky, 1973
• Nothing in biology makes sense
except in the light of evolution.
• The American Biology Teacher, 1973; 35:
125-129.
19

Pierre Teilhard de Chardin
• “Evolution is a light which illuminates all
facts, a trajectory which all lines of thought
must follow - this is what evolution is”.
• Pierre Teilhard de Chardin - one of the
greatest thinkers of our time.
• Teilhard was a creationist, but such who
understood that Creation is realized in this
world through evolution.
20

Peter Brian Medawar
• “For a biologist, the alternative
to thinking in evolutionary terms
is not to think at all”
• Medawar P., Medawar J.S, The Life Science:
Current Ideas in Biology, London: Wildwood
House, 1977
21

• Swynghedauw B.
• Nothing in medicine makes sense except in the light
of evolution: A Review.
• P. Pontarotti (ed.), Evolutionary Biology from Concept to
Application, Springer-Verlag Berlin Heidelberg, 2008; pp. 197-207.
• Varki A.
• Nothing in medicine makes sense, except in the light of evolution.
J. Mol. Med., 2012; 90:481–494
• “Understanding human evolution, where we
came from, is very important to understanding
who we are and where we’re going.”
22

• Kalinowski S.T., Leonard M.J., Andrews T.M.
• Nothing in evolution makes sense except in the light
of DNA
• CBE—Life Sciences Education, 2010; 9: 87–97,
• Natural selection is an inherently difficult process
for students to grasp.
• “It is almost if the human brain were specifically
designed to misunderstand Darwinism.” Dawkins
(1986)
23

Weiss K.M., Buchanan A.V., Lambert B.W.
The Red Queen and Her King: Cooperation at all Levels of Life.
Yearbook of Physical Anthropology, 2011; 54: 3–18.
• BEYOND EVOLUTIONARY THEORY
• An excessive focus on evolution as the only thing that ‘‘makes sense in
biology’’ to quote Dobzhansky’s famous assertion, draws attention away
from things that characterize much more of life, much more of the time.
• What goes on in the lives of cells, and the organisms they comprise, can
not only help us understand what happens on the evolutionary time
scale, but also relates to a number of other questions that evolutionary
theory does not address.
• Dobzhansky’s assertion was good for resisting creationism in schools, but
as biology it is manifestly.
24

One of controversial evolutionary argument was
concerned to AB0 blood group system
25

AB0 and diseases
• The only associations between AB0 blood
groups and malignant neoplasms,
thrombosis, peptic ulcers, bleeding, bacterial
and viral infections are still regarded as
statistically “proven“.
• Alas, these associations have no clinical
(practical) importance due to low values of
odds ratio (OR) which do not exceed the
value of OR = 1.5.
26

Associations between AB0 blood groups and diseases,
which are considered to be statistically “proven”
Medical condition A > 0 0 > A B/AB > A/0 OR
Malignancy X 1.2 – 1.3
Thrombosis X
Peptic ulcers X 1.2 – 1.4
Bleeding X 1.5
E. coli / Salmonella X
27

Edgren G, Hjalgrim H., Rostgaard K., Norda R, Wikman A, Melbye M., Nyré O.
Risk of gastric cancer and peptic ulcers in relation to AB0 blood type: a cohort study
Am. J. Epidemiol., 2010. – Vol. 72. – P. 1280–1285
Blood group
Donors
Cases with the
gastric cancer
N f with 99% CI Cause N f with 99% CI
A 478633 0.4380.4400.441 Deficit 331 0.410.470.52
AB 57904 0.05260.05320.0539 Excess 45 0.0410.0670.102
B 122819 0.1120.1130.114 Deficit 66 0.070.100.14
0 428978 0.3930.3940.396 Excess 246 0.310.370.43
HWE, Pval 9∙10-83 0.12
Homoge-neity
Pval 0.034
BF01 68.5
The authors argue that this large Danish-Swedish cohort study confirms the
“association” between blood group A and gastric cancer. Actually in control group
the deviations from the HWE is observed due to deficiency of allele A. Moreover, the
difference between groups is statistically minor and clinically negligible: OR = 1.15.
28

Rubanovich A.V., Khromov-Borisov N.N. Theoretical Analysis of the
Predictability Indices of the Binary Genetic Tests. Russian Journal of
Genetics: Applied Research, 2014, Vol. 4, No. 2, pp. 146–158.
• It is customary to interpret OR values ≤ 1.5 as virtually worthless,
from 1.5 to 3.5 - very low, from 3.5 to 9.0 - low, from 9.0 to 32 –
moderate, from 32 to 100 – high and >100 - very high.
• Our theoretical study shows that when OR < 2.2, marker has
notoriously low predictive performance in all respects and at all
frequencies of occurrence of the disease and the marker.
• The marker can be a good classifier, if OR > 5.4, provided that its
population frequency is sufficiently high (pM > 0.3).
• In practice, this means that to these inequalities must satisfy the
lower bounds of the confidence interval for the estimated value of
OR.
• Earlier, similar values of critical levels for observed effects in the
genetics of predispositions were offered for the relative risk (RR <
2 and RR > 5, respectively).
29

Zhang B., Beeghly-Fadiel A., Long J., Zheng W., Genetic Variants Associated
with Breast Cancer Risk: Comprehensive Field Synopsis, Meta-Analysis, and
Epidemiologic Evidence. Lancet Oncol., 2011; 12(5): 477–488
• More than 1,000 candidate-gene in breast cancer association studies have
been published in the last two decades, which have evaluated more than
7,000 genetic variants.
• While some of these variants may represent true associations with breast
cancer risk, many more are false-positive associations which fail to replicate
among additional study populations.
• 51 variants in 40 genes showed statistically significant associations with
breast cancer risk.
• Cumulative epidemiologic evidence for an association with breast cancer risk
was graded as strong for 10 variants in six genes (ATM, CASP8, CHEK2, CTLA4,
NBN, and TP53),
• moderate for four variants in four genes (ATM, CYP19A1, TERT, and XRCC3),
and
• weak for 37 additional variants.
• Additionally, in meta-analyses that included a minimum of 10,000 cases and
10,000 controls, convincing evidence of no association with breast cancer
risk was identified for 45 variants in 37 genes.
30

High- and moderate-penetrance breast cancer susceptibility genes
Gene Variants
Relative Risk,
RR
Population
Frequency
(%)
BRCA1 Multiple mutations >10 0.1
BRCA2 Multiple mutations >10 0.1
TP53 Multiple mutations >10 <0.1
PTEN Multiple mutations >10 <0.1
ATM Truncating and missense mutations 2–4 <0.5
CHEK2 1100delC 2–5 0.7
BRIP1 Truncating mutations 2–3 0.1
PALB2 Truncating mutations 2–5 <0.1
31

Sources of uncertainty of
genetic chiromancy and
predictions
32

P = G + E + (G x E) + (Gj x Gk) + …
• We should remember the fundamental statement:
• Phenotype (P) is the product of the interaction
between genotype (G) and environment (E).
• Such interaction can be primitive (linear, additive)
or sophisticated (nonlinear, multiplicative,
compensatory, neutralizing, opposite, etc.).
• Some of them can be cryptic, which are not
exhibited under normal conditions and so it is hard
to reveal them.
33

Environment – internal and external:
«Exposome»
Wild C.P. The exposome: from concept to utility.
International Journal of Epidemiology, 2012; 41: 24–32
34

Padmanabhan S., Newton-Cheh C., Dominiczak A.F. Genetic basis of blood pressure
and hypertension. Trends in Genetics, 2012; 28(8): 1–12
(a) Hypertension is caused by one mutation and occurs in discrete
subpopulations. (b) There is no clear distinction between hypertension and
normotension. Hypertension is extreme variant of the continuum and has a
polygenic nature.
35

Heritability and the environment
Components of genetic and
environmental variability are clearly
distinct
Continuity as an interpenetration of
genetic and environmental
variability
36

There are no common disorders —
just the extremes of quantitative traits
• We predict that research on polygenic liabilities
• will eventually lead to a focus on quantitative
• dimensions rather than qualitative disorders.
• The extremes of the distribution are important medically
and socially, but we see no scientific advantage in reifying
diagnostic constructs that have evolved historically on the
basis of symptoms rather than aetiology.
• A more provocative way to restate our argument is that from
the perspective of polygenic liability, there are no common
disorders — just the extremes of quantitative traits.
• Plomin R., Haworth C.M.A., Davis O.S.P. Common disorders
are quantitative traits. Nature Rev. Genet., 2009. – Vol. 10. –
P. 872-878.
37

Alexey Matveyevich Olovnikov
Алексей Матвеевич Оловников
• Aging as an universal
chronic “disease of
quantitative traits”: cell
aging and RNA-dependent
ion-modulated gene
expression genes.
• Биомедицинский журнал
Medline.ru
• Том 4, СТ. 28 (стр. 31)
• Февраль, 2003 г.
38

Aging is an universal genetic “disease of
quantitative traits”
• During the aging of humans and animals no expression of
principally new macromolecules is observed.
• All that occurs during aging, is not a qualitative but
quantitative change of various traits, whose number is
enormous.
• If aging is really a disease of quantitative
• traits, it is appropriate to target the
• biogerontological research to those key
• molecular mechanisms that underlie the
• regulation of quantitative traits in eukaryotes.
39

• The larger the number of factors, both genetic and
environmental, influencing given trait (disease or
propensity), the greater the unpredictability of the
manifestation of this trait.
• The same disease can be determined by different versions of
different genes.
• The same gene may be involved in the development of
various diseases and syndromes.
• Some versions of given gene (DNA sequence variants,
alleles) may predispose to one disease, and other its
versions - to another disease.
40

One disease can be influenced by
many genes
G-1 G-2 G-3 . . . G-k
Disease
One gene can affect many
diseases
Gene
D-1 D-2 D-3 . . . D-k
41

• Each gene affects many traits, and each trait is determined by many
genes.
• Sources of uncertainty and unpredictability:
• Reduced penetrance
• Variable expressivity,
• Pleiotropy
• Terms penetrance and expressivity were introduced by Oskar Vogt and
Elena (Helena) and Nikolai Timofeev-Resovsky in 1926.
• Vogt O. Psychiatrisch wichtige Tatsachen der zoologisch-botanischen
Systematik. Zeitschrift für die gesamte Neurologie und Psychiatrie, 1926;
101:805-32
• Timofeeff-Ressovsky H.A., Timofeeff-Ressovsky N.W. Über das
phänotypische Manifestieren des Genotyps. II. Über idio-somatische
Variationsgruppen bei Drosophila funebris. Wilhelm Roux‘ Archiv fur
Entwicklungsmechanik der Organismen, 1926; 108: 148-70
42

Variable expressivity - Syndactyly
Complete Partial

Polydactily – the character with incomplete
penetrance as well as variable expressivity

Reduced penetrance
• Penetrance refers to the proportion of people with a particular genetic
change (such as a mutation in a specific gene) who exhibit signs and
symptoms of a genetic disorder.
• If some people with the mutation do not develop features of the
disorder, the condition is said to have reduced (or incomplete)
penetrance.
• Reduced penetrance often occurs with familial cancer syndromes.
• For example, many people with a mutation in the BRCA1 or BRCA2 genes
will develop cancer during their lifetime, but some people will not.
• Doctors cannot predict which people with these mutations will develop
cancer or when the tumors will develop.
• This phenomenon can make it challenging for genetics professionals to
interpret a person’s family medical history and predict the risk of passing
a genetic condition to future generations.
45

Variable expressivity
• Variable expressivity refers to the range of signs and
symptoms that can occur in different people with the same
genetic condition.
• For example, the features of Marfan syndrome vary widely—
some people have only mild symptoms (such as being tall
and thin with long, slender fingers), while others also
experience life-threatening complications involving the heart
and blood vessels.
• Although the features are highly variable, most people with
this disorder have a mutation in the same gene (FBN1 –
fibrillin-1).
• If a genetic condition has highly variable signs and
symptoms, it may be challenging to diagnose.
46

Penetrance and expressivity
48

Pleiothropy
• Pleiotropy occurs when one gene influences
multiple, seemingly unrelated phenotypic traits.
• Pleiotropic gene action can limit the rate of
multivariate evolution when natural
selection, sexual selection or artificial selection on
one trait favours one specific version of the gene
(allele), while selection on other traits favors a
different allele.
• The underlying mechanism of pleiotropy in most
cases is the effect of a gene on metabolic pathways
that contribute to different phenotypes.
49

Pleiothropy
• One of the most widely cited examples of pleiotropy in
humans is phenylketonuria (PKU).
• Phenylketonuria (PKU) is an autosomal
recessive metabolic genetic disorder characterized
by mutations in the gene for the hepatic
enzyme phenylalanine hydroxylase (PAH).
• A defect in the single gene (PAH) that codes for this enzyme
therefore results in the multiple phenotypes associated with
PKU, including mental retardation, tumors, eczema, mousy
odor and pigment defects that make affected individuals
lighter skinned.
50

Antagonistic Pleiotropy
• For example, in humans, the p53 gene directs damaged cells
to stop reproducing, thereby resulting in cell death.
• This gene helps avert cancer by preventing cells with DNA
damage from dividing, but it can also suppresses the division
of stem cells, which allow the body to renew and replace
deteriorating tissues during aging.
• This situation is therefore an example of antagonistic
pleiotropy, in which the expression of a single gene causes
competing effects, some of which are beneficial and some of
which are detrimental to the fitness of an organism.
51

Pleiotropy and homeostasis
• Pleiotropy is one of the main mechanisms for
maintaining homeostasis especially when it is
mutually opposite (“compromise” or
“compensatory”) and / or antagonistic.
• Homeostasis is a central principle of living systems;
it is the relatively stable state of equilibrium, or the
tendency toward such a state, between different
but interdependent elements and subsystems of an
organism.
52

Example: APOE
• The case of APOE provides a familiar example
of a common variant with well-established
cross-phenotype effects.
• The APO*ε4 allele is a known risk factor for
both atherosclerotic heart disease and
Alzheimer’s disease but has also been shown
to exert a protective effect on risk of age-related
macular degeneration.
53

Example: The multiplicity of physiological functions of
ACE
• Angiotensin-converting enzyme (ACE), is not only
the blood pressure monitors, but it also participates
in the fertilization process, the formation of
immune cells, the development of atherosclerosis.
• Its high expression in macrophages, immune cells,
prevents the formation of malignant tumors.
• Therefore, the use of ACE inhibitors can provoke
cancer and Alzheimer's disease.
54

Nawaz S.K., Hasnain S. Pleiotropic effects of ACE polymorphism.
Biochemia Medica, 2009; 19(1): 36–49.
Diseases allegedly “associated” with the indel dimorphism in the ACE gene
Association present Association absent Controversial
Diabetic nephropathy Type 2 diabetes Hypertension
Atherosclerosis Diabetic retinopathy Coronary heart disease and
stroke
Alzheimer disease
Allele D is “preventing”
Gastric cancer Colorectal cancer
Parkinson’s disease Systemic lupus erythematosus Longevity
Breast cancer
Oral cancer
Treatment of osteoporosis
The polymorphism is due to insertion (I allele) or deletion (D allele) of a 287
bp fragment in intron 6 of the ACE gene in chromosome 17.
Brown highlighted are associations that in Russia continue to be considered
certainly established.
55

Ubiquitous VDR and ESR - receptors of “vitamin” D and
estrogen
• VDR activity extends far beyond the metabolism of calcium
and parathyroid hormone (PTH).
• It participates in the transcription of 900 genes, some of
which are key to health, such as MTSS1 (metastasis
suppressor), as well as key components of innate immunity
(cathelicidin antimicrobial peptide, beta-defensins, TLR2 -
toll-like receptor, etc. ).
• VDR role in innate immunity is unique to humans.
• No other animal model (e.g. mouse) did not develop such an
evolutionary function for this receptor.
• Estrogen receptor ESR directly or indirectly is responsible for
the expression of 6,000 genes, i.e. 19% of the entire genome.
56

Sivakumaran S., Agakov F., Theodoratou, E., Prendergast J.G., Lina Zgaga L., Manolio
T., Rudan I., McKeigue P., Wilson J.F., Campbe H. Abundant Pleiotropy in Human
Complex Diseases and Traits. Am. J. Hum. Genet., 2911; 89: 607–618
Demonstration of «abundant» pleiotropic action of genes
associated with Crohn’s disease
58

Conclusion
• Click the filly on the
nose - it will wag its tail
• Koz’ma Petrovich
Prutkov
59

Microbiome and metagenome
• It is estimated that the human intestinal microflora are
composed of between 1013 and 1014 microorganisms
(comparable with the number of our own cells), comprising
>1000 bacterial species).
• The metagenome of this so-called microbiome has at least
100 times as many genes as our own genome.
• The microbiome can be thought of as an additional organ,
which is estimated to weight 1 kg in an adult human and is
mutalistic to humans (and the commensal microflora that
inhabit the host).
60

Influence of the micribiote on the physiology of
the human dody
61

Metabolome
• As the consequence, the microbiome provides the human
host with additional metabolic functions, described as the
“metabolome”.
• It:
• (i) provides a barrier for colonization of pathogens;
• (ii) exerts fermentation of non-digestible fibers, salvage of
energy and synthesis of vitamin K; and
• (iii) stimulates the development of the immune system.
• It was stated earlier that, from human genome sequence
analyses, there are predicted to be 2645 metabolites in the
human metabolic network.
• This number will inevitably need revising keeping in mind
prokaryotic-derived metabolites in humans.
62

• In summary, there are 1014 total gastrointestinal tract (GI)
bacteria….
• If we assume that one mutation in every 108 bacterial
divisions is a viable mutation, 1014 total bacteria in the GI
tract theoretically will produce 106 newly mutated viable
bacteria at every division cycle.
• It is estimated that the bacteria in the GI tract divide every
20 minutes.
• This generation of large numbers of newly mutated bacteria
at every division cycle allows the indigenous GI microflora to
adapt rapidly to GI environmental changes.
63

We may be born 100% human but will die 90%
bacterial—a truly complex organism!
• The microbiome (the intestinal microflora as well as those
bacteria found on the skin, in airways, and in the urogenital
tract) may play an important role in maintaining human
health.
• This interaction of the microbiome with humans suggests
that the human be considered as a superorganism, where
we are in fact a human-microbe hybrid.
• The womb is sterile, and so babies are born with gnotobiotic
gastrointestinal tracts.
• Its bacteria are mainly maternally acquired, and this process
is largely achieved in the first year of life.
64

Unpredictability of genetic predispositions
• Knowing the genotype we cannot unambiguously
predict the phenotype, and vice versa:
• Knowing the phenotype it is not possible to predict
unequivocally the genotype.
• The validity of the uncertainty principle in genetics
is extremely clear:
• even knowing the genome sequence of the person,
we will never be able to predict many of its
features, e.g., to predict the circumference of his
waist.
65

Janssens A.C.J.W., van Duijn C.M. An epidemiological perspective on
the future of direct-to-consumer personal genome testing.
Investigative Genetics, 2010; 1:10
66

Amizing phenomena of genetic predisposition
testing
• Fuzzy phenotypes.
• Winner's curse.
• Discordant conclusions.
• Genotyping errors.
• Mania of secrecy.
• Multi- and oppositely directed pleiotropy.
• Inadequate statistical analysis.
• Publication bias.
67

Fuzzy (poorly distinguishable, not alternative)
phenotypes – the shadow of Lamarck
• Low efficiency of results in the genetics of
predispositions already lies in the uncertainty of the
determination (diagnosis) of the studied trait.
• For example, how to distinguish an “athlete” from a
“smug”?
• As a control group of non-athletes the persons
leading a sedentary lifestyle are often sampled.
• But if you think about it, this is pure Lamarckism
with its asserting the influence of "exercise" and
“non-exercise“ of an organ on its evolutionary
destiny.
68

Winner's curse
• Too often, initially promising discoveries that
typically cause great excitement, are not
reproduced in subsequent studies.
• This phenomenon is called the “winner's
curse”.
69

Discordant conclusions
70
Ng P.C., Murray S.S., Levy S., Venter J.C. An agenda for personalized medicine. Nature, 2009;
461: 724-726. About one half of conclusions presented by two companies (23andMe and
Navigenics) contradicts one another.

Imai K., Kricka L.J., Fortina P. Concordance Study of 3 Direct-to-Consumer
Genetic-Testing Services. Clin. Chem., 2011; 57(3): 518–521
Relative disease risk assigned by 3 DTC services for a series of diseases evaluated by
all 3 services. Values in parentheses indicate the number of SNPs analyzed .
71

Kalf R.R.J., Mihaescu R., Kundu S., de Knijff P., Green R.C., Janssens
A.C.J.W. Variations in predicted risks in personal genome testing for
common complex diseases. Genetics in Medicine, 2014; 16(1): 85-91
• Predicting risks vary significantly between
companies due to differences in the sets of
SNPs used and the average values of the
population risk, as well as the differenses in
the formulas used to calculate the risks.
72

Comparison of risks for three multifactorial diseases predicted
by 23andMe, deCODEme and Navigenics
73

Comparison of risks for three multifactorial diseases predicted
by 23andMe, deCODEme and Navigenics
74

Adams S.D., Evans J.P., Aylsworth A.S. Direct-to-Consumer Genomic
Testing Offers Little Clinical Utility but Appears to Cause Minimal
Harm. N. C. Med. J.m, 2013; 74(6): 494-499
75

CURRENT ONCOLOGY, 2009; 16(1): 56-58
78

Pharmacogenomics
• Lenzini P., Wadelius M., Kimmel S., Anderson J.L., Jorgensen
A.L., Pirmohamed M., Caldwell M.D., Limdi N., Burmester
J.K., Dowd M.B., Angchaisuksiri P., Bass A.R., Chen J.,
Eriksson N., Rane A., Lindh J.D., Carlquist J.F., Horne B.D.,
Grice G., Milligan P.E., Eby C., Shin J., Kim H., Kurnik D., Stein
C.M., McMillin G., Pendleton R.C., Berg R.L., Deloukas P.,
Gage B.F.
• Integration of genetic, clinical, and INR data to refine
warfarin dosing.
• Clin. Pharmacol. Ther., 2010; 87(5): 572-578.
80

Calculation of individual warfarin dose
• Pharmacogenetic dose (mg/week) =
• EXP [3,10894 − 0,00767  age − 0,51611  ln(INR) − 0,23032  VKORC1-
1639 G>A − 0,14745  CYP2C9*2 − 0,3077  CYP2C9*3 + 0,24597  BSA +
0,26729  Target INR − 0,09644  African origin − 0,2059  stroke −
0,11216  diabetes − 0,1035  amiodarone use − 0,19275  fluvastatin
use + 0,0169  dose−2 + 0,02018  dose−3 + 0,01065  dose−4].
• Clinical dose (mg/week) =
• EXP [2,81602 − 0,76679  ln(INR) − 0,0059  age + 0,27815  target INR −
0,16759  diabetes + 0,17675  BSA − 0,22844  stroke − 0,25487 
fluvastatin use + 0,07123  African origin − 0,11137  amiodarone use +
0,03471  dose−2 + 0,03047  dose−3 + 0,01929  dose−4].
81

82
Pharmacogenetically predicted warfarin
dose
Clinically predicted warfarin dose

FDA warns and accuses: genetic testing is not
scientifically justified
• Food and Drug Administration USA (FDA) has sent a
WARNING LETTER to 17 companies (23andMe, Navigenics,
deCODEme, EasyDNA and others) who are engaged in
genetic testing, an order to cease their activities because of
the lack of scientific evidence and the inability to accurately
predict the risk of diseases.
• http://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/InVitroDiagnostics/default.htm
23.04.2014 83

Honesty: article retraction due to genotyping errors
• Sebastiani P., Solovieff P.N., Puca A., Hartley S.W., Melista E.,
Andersen S., Dworkis D.A., Wilk J.B., Myers R.H., Steinberg
M.H., Montano M., Baldwin C.T., Perls T.T. Retraction.
Science, 2011; 333: 404
• After online publication of our Report “Genetic Signatures of
Exceptional Longevity in Humans“ in Science Express, July 1,
2010, we discovered that technical errors and an inadequate
quality control protocol had introduced errors in our results.
• We are voluntarily retracting the original manuscript and are
pursuing alternative publication of the corrected results.
• We will be happy to discuss our amended findings as soon as
they are published.
84

Growth of the number of publications on genotyping
error
85

Fang F.C., Steen R.G., Casadevall A. Misconduct accounts for the
majority of retracted scientific publications. PNAS, 2012;
109(42):17028–17033.
86

• Too many sloppy mistakes are creeping into
scientific papers.
• Lab heads must look more rigorously at the
data — and at themselves.
87

Yong E. In the wake of high-profile controversies, psychologists are facing
up to problems with replication. Nature, 2012; 485: 298-300
• Publication bias.
• A literature analysis across
disciplines reveals a
tendency to publish only
‘positive’ studies – those
that support the tested
hypothesis.
• Psychiatry and psychology
are the worst offenders.
88

Two fundamental questions
• From evolutionary point of view, genetics of predispositions
has to answer two basic questions:
• 1. Is the natural genetic polymorphism identified with
modern genomics proved to be the result of neutral
evolution or whether it is an aggravated genetic (mutation)
load determining the susceptibility to common diseases,
which inexplicably has not been culled by the natural
selection well-timed?
• 2. Are joint effects of different predisposing alleles
synergistic or at least additive when combined in a single
genotype, or they are mutually neutralized?
89

• Evolutionary and population arguments help us to
understand that the «genetics of predispositions» studies
natural balanced genetic polymorphism, i.e. not the newly
formed alterations of genes (mutations), but alleles passed
natural selection and fixed in human populations; not
anomalies, not pathological or pathogenic variants of the
genome are investigated, but infinite number of its natural,
«normal» variants.
• Thus the answer on the first of two questions is:
• Evolutionary medical genomics testifies that the vast
majority of polymorphic variants of genes (alleles) that are
observed in the genomes of modern human populations are
selectively neutral.
90

• Indeed, it appears that the coding, i.e. functionally
important regions in the human genome, show a much
lower degree of variation than non-coding, i.e. whose
function is unknown.
• The absolute number of synonymous variants outnumbers
nonsynonymous (missense) variants, despite the fact that
the number of positions at which non-synonymous variants
can occur, is 3 times higher than the position with the
possibility of synonymous mutations.
• Proportion of synonymous variants is 4 times greater than
non-synonymous (80% and 20% respectively).
91

• In general, the neutralist evolutionary views lead to the
conclusion that historical adaptive evolutionary events are
not the source of diseases.
• On the contrary, evolution is a source of stability and the
reason why human beings so successfully exist in widely
varying conditions.
• Neutrality and balance explain the fact that the predisposing
genotypes are found both in persons with the disease
(patients) and in persons without the disease (“healthy”),
and the only difference is observed in their frequencies in
groups of subjects with given disease and without it.
• That is, certainly the presence of predisposing alleles in the
genotype of given person is not indicative of the inevitable
presence of given disease or other propensity in the preset
or its occurrence in the future.
92

• The second principal question:
• Are effects of different predisposing alleles
synergistic or at least additive when being
combined in a single genotype, or they are mutually
neutralized?
• The answer is:
• In many cases, the effects of various predisposing
alleles are mutually neutralized through the
mechanisms of opposite (antagonistic) pleiotropy
and homeostasis.
93

Frequency distributions in two sample of persons with extreme
values of blood pressure for 35 alleles predisposing to
hypertension
94
The distributions are almost completely overlapping.

Paynter N.P., Chasman D.I., Pare G., Buring J.E., Cook N.R., ScD, Miletich J.P.,
Ridker P.M. Association between a literature-based genetic risk score and
cardiovascular events in 19,313 women. JAMA, 2010; 303(7): 631–637.
GRS - genetic risk score – do not improve risk prediction
for cardiovascular diseases.
95

Thromboembolism, 11 markers (Kapustin S.I., 2007)
96
0 2 4 6 8 10 12
125
100
75
50
25
0
Число предрасполагающих аллелей
Численность
593 patients with venous thrombosis,
Control group of 225 persons
Pval = 0.046 (Mann-Whitney test); Pval = 0.52 (χ2); BF01 = 105

Bayes’ theorem in action
97
 
  P  D  P  g |
D

1
           


 
P D P g D P D P g D
PPV P D g
| |
|
1 1
1 1
  P  D   P  g | D   P  g |
D


1 2
                 


 
P D P g D P g D P D P g D P g D
PPV P D g g
| | | |
| ,
1 2 1 2
1,2 1 2
To calculate predictive probabilities like PPV we have to
know pretest (prior) probability of the presence of the
disease P(D+) which is called prevalence, Prev and
“counter-prevalence” coPrev = P(D-) = 1 – P(D+)

Artificial, but rather typical example
• Let the prevalence of the disease with the elements of hereditary predisposition
(D) in a population is
• P(D+) = 1%.
• Assume that the proportion of carriers of the genotype (allele or haplotype) g1,
predisposing to this disease equals
• P(g1|D+) = 20%,
• and it is two times higher than for individuals without this disease:
• P(g1|D-) = 10%.
• This corresponds to the values of the risk ratio RR = 2 and the odds ratio OR = 4,4.
• According to Bayes formula the probability of this disease in patients with the
genotype g1 at approximately 2-fold higher than its prevalence:
• PPV1 = P(D+|g1) = 1,98% ≈ 2%.
• Obviously, on the basis of such a small value it is hard to get convincing
predictions about the presence or development of this disease in a particular
individual.
98

Predictive values of several predisposing
genetic markers
Number of predisposing
loci in a genotype
PPV
Proportion of carriers of the
predisposing genotype in population
1 0.020 0.1
2 0.039 0.01
3 0.075 0.001
4 0.14 0.0001
5 0.24 10-5
6 0.39 10-6
7 0.71 10-7
8 0.84 10-8
9 0.91 10-9
10 0.95 10-10
99

Odds ratios (OR) for the 2 type diabetes corresponding to the
number of the predisposing allele in the genotype
100

Persons at high genetic risk are very rare
• Really, the larger the number of predisposing alleles in the
genotype, the higher the risk of disease.
• Theoretically it is possible to identify people with a very high
risk of the disease, but in practice they will be extremely
rare.
• And it is difficult if impossible to prove that given rare
combination of predisposing alleles is responsible for the
presence of given disease in given person.
• In vast majority of persons the risk of disease only slightly
higher than the average risk of disease in the population.
101

Hirschhorn J.N. Genomewide Association Studies — Illuminating Biologic Pathways
N. Engl. J. Med., 2009; 360(17): 1699-1701.
• Many newly identified loci do not implicate genes with
known functions.
• It is hardly surprising that we do not yet understand the
biologic import of every recently associated locus: the
associations sometimes do not point unambiguously to a
particular gene, and even genes that are clearly implicated
are often unannotated with respect to function.
• With regard to prediction, the common variants described
by genomewide association studies almost universally have
modest predictive power, and for most diseases and traits,
these variants in combination explain only a small fraction
of heritability.
• The success of genomewide association studies is not tied to
prediction.
102

• The number of markers associated with sport performance is
estimated as about 200.
• Let us imagine that we will be able to gather in one genome
almost all known alleles predisposing to a particular sport
pursuits.
• Obviously, due to the non-additivity of interactions of
intergenic and environmental factors the athletic
performance of a person with such genotype will not be
proportional to the number of predisposing alleles.
• It seems to be unlikely that combining 200 predisposing
alleles in one genome will result in a 200-fold increase in
sports performance of such persons.
• And due to opposite directed pleiotropic action of these
alleles will our Superman appear to become a “superidiot”?
103

Yannis Pitsiladis
University of Glasgow.
• “Currently, the predictive ability of sports
genetics is zero.
• There is no direct evidence for the existence of genetic
indicators of the success of athletes.
• The effectiveness of an athlete depends primarily on the
socio-economic, cultural and environmental factors.
• So stopwatch predicts much better athletic performance
runner than the whole genetics.”
• http://news.menshealth.com/why-kenyans-keep-winning-marathons/2011/06/03/
104

The predictive ability of clinico-genetic
certification
105

The main measures of the quality of diagnostic
test with the binary outcomes
• Even if the genetic association is statistically highly
significant, it certainly raises the question of how
useful is this information for risk assessment and
prediction of disease?
• For these purposes it is necessary to measure well-known
indicators of the diagnostic test quality, such
as the sensitivity (Se), specificity (Sp), positive
prediction value (PPV), negative prediction value
(NPV), positive likelihood ratio (LR[+]) and negative
likelihood ratio (LR[+]).
106

The main indices of the detection capability of the index
diagnostic test
Index test
Gold standard
Result:
Disease is present, D+ Disease is absent, D-Result:
Positive,
T+
Sensitivity – probability of
the positive in a person
with the disease
Se = P(T+|D+)
Counter-specificity –
probability of the positive in
a person without the
disease
coSp = P(T+|D-) = 1 – Sp
Negative
T-Counter-
sensitivity –
probability of the negative
in a person with the disease
coSe = (T-|D+) = 1 – Se
Specificity – probability of
the negative in a person
without the disease
Sp = P(T-|D-)
107

The main indices of the prediction сapability of the index
diagnostic test
Index test
Gold standard
Result:
Disease is present, D+ Disease is absent, D-Result:
Positive,
T+
Positive predictive value –
probability of presence the
disease in a person with
positive
PPV = P(D+|T+)
Positive counter-predictive
value – probability of
absence of the disease in a
person with positive
coPPV = P(D-|T+) = 1 – PPV
Negative
T-Negative
counter-predictive
value – probability of
presence of the disease in a
person with negative
coNPV = P(D+|T-) = 1 – NPV
Negative predictive value –
probability of the absence of
the disease in a person with
negative
NPV = P(D-|T-)
108

N.B. – nota bene
•P(D+|T+) ≠ P(T+|D+)
109

Lotufo P.A., Chae C.U., Ajani U.A., Hennekens C.H., Manson J.A.E. Male
pattern baldness and coronary heart disease: The Physician's Health Study.
Arch. Intern. Medicine, 2000; 160: 165-171.
CAD
Total Predictiveness
Alopecia Yes, D+ No, D-Yes,
M+
127
0,0700,0940,123
1224 1351 PPV = P(D+|M+)
= 0,070,100,12
No, M-
548
0,0580,0670,077
7611 8159 NPV =
P(D-|M-)
= 0,920,930,94
Total
675 8835 9510 PrevD = P(D+)
= 0,060,070,08
Detectability
Se = P(M+|D+)
= 0,140,190,24
Se = P(M-|D-)
= 0,850,860,87
PrevM = P(M+) =
0,130,140,15
LR[+] =
P(M+|D+)/P(M+|D-)
= 1,021,361,77
LR[-] =
P(M-|D-)/P(M-|D+)
= 1,001,061,14
AUC = (Se + Sp)/2 = 0,500,530,56
Homogeneity
Pval = 0,00058 ≈ 6∙10-6
BF10 = 18,9
Association OR = 1,021,452,01
111

Better to see once
• Results of statistical quality control of
diagnostic tests is useful to visualize as
predictive graphs.
• Examples are shown in figures below.
• The spreadsheet created by R.G. Newcombe
PPVNPV.xls may be used
• http://medicine.cf.ac.uk/media/filer_public/
2012/11/01/PPVNPV.xls
23.04.2014 112

Lotufo P.A., Chae C.U., Ajani U.A., Hennekens C.H., Manson J.A.E., Male pattern baldness and coronary heart
disease: The Physician's Health Study, Arch. Intern. Med. 2000; 160(2): 165-71.
Simon A., Worthen D. M., Mitas J. A.1979. An evaluation of iridology // JAMA, V. 242, N 1, P. 1385-1389.
Alopecia and CAD Iridology and renal failure
113
127/1224/548/7611
LR[+] = 1.01.41.8; LR[-] = 1.01.061.1
29/59/19/36
LR[+] = 0.71.01.4; LR[-] = 0.71.01.5
23.04.2014

• Since the time of Hippocrates (c. 460 – c. 370 BC) it is
known that eunuchs do not go bald when they become
eunuchs before the age of 25.
• It’s unlikely that any doctor on the basis of these data will
recommend to young men to have children up to 25 years
and then became eunuch not to go bald and thus to
reduce the risk of developing coronary heart disease by
2%.
• Nevertheless, it is very similar to the recommendations of
medical geneticists, most of which are too often based on
clinically insignificant values of the indices of predictive
abilities of genetic testing.
• Rarely odds ratios in these studies exceed the value
• OR > 2.
114

Druzhevskaya A.M, Ahmetov I.I., Astratenkova I.V., Rogozkin V.A. 2008. Association of the ACTN3 R577X
polymorphism with power athlete status in Russians. Eur. J. Appl. Physiol., 2008; 103: 631–634.
Кундас Л.А., Жур К.В., Бышнев Н.И. и др. Анализ молекулярно-генетических маркеров, ответственных
за устойчивость к физическим нагрузкам, у представителей академической гребли. Молекулярная и
прикладная генетика: сб. науч. тр. Институт генетики и цитологии НАН Беларуси; (гл. ред. А.В.
Кильчевский). 2013. - Минск: ГНУ «Институт генетики и цитологии НАН Беларуси», Т. 14. – C. 101-105.
Gene ACTN3 and elite athletes Gene PPARG and elite rowers
115
455/1027/31/170
LR[+] = 1.01.11.1; LR[-] = 1.42.23.7
3/3/21/147
LR[+] = 0.95.840; LR[-] = 1.11.21.6
23.04.2014

Mayeux R., Saunders A.M., Shea S., et al. 1998. Utility of the apolipoprotein E genotype in the
diagnosis of Alzheimer’s disease. N. Engl. J. Med., 1998; 338: 506-511.
Mäki M., Mustalahti K., Kokkonen J., et al. 2003. Prevalence of celiac disease among children in
Finland. N. Engl. J. Med., 2003; 348: 2517-2524.
Gene APOE and Alzheimer's disease HLA haplotypes and celiac disease
116
1142/133/622/285
LR[+] = 1.72.02.5; LR[-] = 1.71.92.2
54/1357/2/2214
LR[+] = 2.72.52.7; LR[-] = 4.112103
23.04.2014

Banks E., Reeves G., Beral V. et. al. Influence of personal characteristics of individual women on sensitivity and
specificity of mammography in the Million Women Study: cohort study. BMJ, 2004; 329(7464): 477-479.
Kevin P. Delaney K.P., Branson B.M., Apurva Uniyal A. et al. Performance of an oral fluid rapid HIV-1/2 test:
experience from four CDC studies. AIDS, 2006; 20: 1655–1660.
Mammography and breast cancer Rapid test for HIV OralQuick®
117
629/3885/97/117744
LR[+] = 262729; LR[-] = 5.77.29.3
327/12/1/12010
LR[+] = 4959192141; LR[-] = 451653171
23.04.2014

Temptations which should be disposed of
• (1) Catastrophism (or “trillerism") – hypnotizing ourselves and others
that our genome is a dump of hazardous alleles.
• (2) Genetitsism - genetic determinism - the blind, fanatical belief in the
omnipotence of genes like the statement: "Genetics - the basis of
medicine”.
• (3) Eugenics - an underlying desire to improve human nature and to
select for a breed of "good" or "right" people, "elite", such as e.g.,
athletes.
• (4) The commercialization of basic science, which, God forbid, may fall to
criminalization.
• Fundamental science loses its chastity and becomes mercenary
(corrupt).
• It is pushing on this slippery slope (“on the street") by the science
administrators who require science to be self-supporting.
118

Summary
• Poor reproducibility and low predictive values of the results in the
genetics of predispositions (genetic association studies) become a
systemic problem.
• Results of the statistical quality control of genetic tests in the study
should be supported with the post-test (posterior) predictive
probabilities (PPV and NPV) and likelihood ratios (LR[+] and LR[-]).
• Predictive values of the vast majority of genetic markers differs little
from the population prevalence of the disease.
• This means that such tests despite high statistical significance of their
results are not able to provide clinically important association between
the disease and biomarker.
• As a result, in most cases, recommendations of medical geneticists are
based on clinically negligible (though statistically significant)
recognizablity and predictability of genetic markers.
119

Some practical conclusions
• Genetics is the science of heredity and heredity is the
fundamental property organisms to pass their traits and
peculiarities to the offspring.
• Therefore, results of the genetic association studies should
be confirmed by the studies of genetic predispositions at the
level of least two generations of relatives, i.e. it is
necessarily to analyze families, pedigrees and twins.
• Before engaging in genomics, the registration of genealogies
should be initially introduced into clinical practice.
• It is cheaper and more efficient.
• There exist simple and effective statistical test TDT – Transfer
Disequilibrium Test.
120

Remember family history is still important
even with molecular characterization
121

Genetics of common complex diseases
• Despite the unequivocally strong statistical associations between genetic
variants and complex diseases, their low sensitivity and specificity afford
limited clinical value for disease predisposition testing.
• Among myriad technological advances and gene discoveries, a simple
family history continues to be advocated as a tool for identification of
common disease risk.
• For very common conditions with high heritability, such as
cardiovascular disease, family history is a much stronger predictor of
disease than any single or combination of genetic/genomic markers.
• One model suggests that neither family history nor genetic testing
should be used as a standalone but that the real power for disease
prediction, risk assessment, and differential diagnosis comes from their
combined use.
122

Statistical audit and free access to the data
• Greatly needed is statistical expertise of papers submitted
for publication in biomedical journals.
• Several editorial boards of scientific journals have invited
experts on statistics.
• It is necessary to impute the responsibility of reviewers to
check the correctness of the calculations.
• For this we need to open the initial (raw) data, as is done, for
example, in the journals Science, International Forensic
Sciences: Genetics.
• When the results were published in the press, then the
original data are no longer the intellectual property of the
authors and should be accessible to specialists.
123

Watson’s bell tolls for Oncogenomocs
http://www.utsandiego.com/news/2013/mar/21/nobel-watson-DNA-irish
• "You could sequence 150,000 people
with cancer and its not going to
cure anyone.
• It might give you a few leads,
but it's not, to me, the solution.
• The solution is good chemistry.
• And that's what's lacking.
• We have a world of cancer biology trained to think
genes.
• They don't think chemistry at all."
124

• Evolutionary medical genomics, whether we
realize it or not, is the foundation of genetics
of predispositions whose main goal should
not be personalized prediction of disease
risk, but to develop strategies for its
treatment and prevention on the basis of the
knowledge of its genetic, evolutionary history
and molecular mechanisms.
125

My Teacher: Mikhail Efimovich Lobashev (11.11.1907 – 04.01.1971)
• In science you can do
anything,
• just don’t forget about
the consequences and
the responsibility.
126

Thank you for your attention!
Slides are available for anybody
Department of Physics, Mathematics and Informatics
First Pavlov State Medical University of St Petersburg
8-952-204-89-49 (Теле2); 8-921-449-29-05 (Мегафон)
127

Reference
• Rubanovich A.V., Khromov-Borisov N.N.
• Theoretical Analysis of the Predictability Indices of
the Binary Genetic Tests.
• Russian Journal of Genetics: Applied Research,
• 2014; 4(2): 146–158.
128

Evolutionary arguments in medical genomics

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