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This is a great popular-level work that analyzes the merits of the neo-Darwinian synthesis (i.e. the theory that random mutation + natural selection working through long periods of time created…oops, I used the `C’ word…ahem!…resulted in…the existence of higher forms of life) and shows it to be an illusory solution to the existence of life. Rather than discussing whether or not a completely naturalistic form of evolution happened using such things as the fossil record or experimental laboratory results, Sanford analyzes the merits of the combination of chance and necessity acting on the genome of biological organisms in abstract (i.e. using statistical mathematics). Now, before you jump ship and assume that he is arguing that “the chances of such and such evolving into such and such is one chance in ten to the blah, blah, blah (really big number) power”, like a few creationists have, you’re wrong. Rather, he looks at the basic assumptions of neo-Darwinian evolutionary theory (NDET from now on) and compares them to what actually happens in nature. In other words, he contrasts how the ND assumption and the actual workings of nature differ greatly in their results. I will elucidate in my description of some of the chapters below.

Before I get to the review of the chapters, I would like to comment on something. It has been noted that Sanford is a young-earth creationist, and for some reason, that is like the plague to certain people. However, any honest reader of this book will also note that anyone (i.e. Christian and non-Christian) could have written the first nine (out of ten) chapters. Only in the tenth chapter does he make an argument for the historicity of Scripture. Even if it wasn’t that way, Dr. Sanford, who possesses a doctorate in genetics and the inventor of the gene-gun, deserves to be heard. Now, to the chapters:

Chapter 1
Here, he discusses the basics of genetics (i.e. genes, nucleotides, genotype, phenotype, etc.) and explains what the neo-Darwinian synthesis is. He then goes through and refutes the famous computer algorithm argument used by Dawkins in The Blind Watchmaker.

Chapter 2
Here is where we start getting into the analysis of NDET. Sanford discusses the statistical distribution of mutational effects (i.e. the magnitude of good and bad mutations affecting fitness) and their frequency. Sanford points out a number of differences between NDET and reality:

A. NDET posits that most mutations are neutral. However, Sanford argues that there is no such thing as a truly “neutral” mutation. Rather, most mutations are “near-neutral” (whether increasing fitness or decreasing fitness). Even a single point-nucleotide mutation in a minor area of the genome disrupts the genetic code to some degree (no matter how small). This is key for the rest of his book.
B. The naïve view of mutational distribution is a bell curve (though many Darwinists recognize that the actual distribution found in nature is nothing like it). The real distribution is a Kimura curve (named after the *Darwinist* population geneticist who created it) where the *vast* majority of the curve is near-neutral. Sanford notes that if the normal distribution (i.e. “bell curve”) was true, then an increase in complexity would be inevitable. However, with the Kimura curve, it is hard to see any substantial increase in fitness “getting off the ground” so-to-speak.
C. NDET acknowledges that most mutations are harmful, but doesn’t suggest that the ratio is so small as to never allow an increase in fitness that would affect a population. Contrary to that assumption, the actual ratio, as noted by the population geneticists (most of whom are Darwinists!) whom Sanford cites, is so small that population geneticists don’t even place the beneficiary curve on the distribution graph! The ratio that Sanford cites (again, from the population geneticists) is between 10,000 to 1,000,000 harmful mutations for every one beneficial (though probably closer to the former figure rather than the latter). Sanford chooses to be conservative, and for the rest of the book, he assumes the 10k ratio. Keep this in mind when the next point is cited.
D. NDET assumes that natural selection will take out all of the bad mutations and leave only the good (notice that that was a near quote of Darwin himself). However, citing the population geneticist, Kimura, for support, Sanford notes that there is a “zone of near-neutrality” on both the beneficial and harmful sides of the curve in which natural selection doesn’t select for or against. This is due to the fact that most mutations are point-nucleotide mutations. These only cause an ever-so-slight decrease in fitness that natural selection can’t “see” them 99% of the time. It would be like a single pixel on your television screen going out. Would you really be able to tell a difference? Furthermore, since the beneficiary mutations curve is so small (see point C. above), the “zone of near-neutrality” (a.k.a. the “no-selection box”) covers 99% of the beneficiary mutation side of the distribution! This ensures that natural selection will never see 99% of the good mutations while allowing the bad (which are vastly greater in number) to accumulate. Thus, the genome will suffer from “genetic entropy” (and hence the title of the book).

Now, a typical reply (which is, in fact, found below in one of the negative reviews) is that biologists have witnessed and documented such beneficiary mutations that have given great benefit to organisms in their environment. However, many biologists are becoming aware that the vast majority of these changes in phenotype are due to “pre-programmed” changes in the genome, not random ones as NDET demands. Secondly, as Sanford points out in Appendix 4, many of these “beneficial” mutations actually end up giving the organism a net decrease in fitness (as in the case of homeostasis in cold-climate creatures to warm climates or drug-resistant bacteria) making them deleterious in reality!

Chapter 3
Here, he starts to go into human population genetics. He cites several twentieth century population geneticists who believed that if there were as many as 0.5 deleterious mutations per person per generation, then the human race would be doomed to extinction. He then cites the actual number of 100 deleterious mutations per person per generation! This is a topic that he comes back to in other chapters of his book. However, from now on, I will concentrate on the implications for NDET. Next, he debunks the junk-DNA and pseudo-gene myth (i.e. those genes really do have a function as scientists are now finding out).

Chapter 4
In this chapter, he discusses the actual power of natural selection as found in nature compared to that which is presupposed by NDET. He notes that most biologists see natural selection as a “magic wand” that eliminates any decrease in complexity while preserving all those changes and variations which give an increase in fitness. Here, he points out a few more problems with NDET when it is contrasted with reality:

E. NDET presupposes that each individual nucleotide is selected for or against. This is a necessary presupposition for all (or even most) deleterious mutations to be selected out (since most mutations are point-nucleotide mutations). In reality, however, it is an entire gene that is selected for or against. In combination with the 10,000 bad to good mutation ratio, this will ensure that for every (random) beneficial mutation that occurs on a gene, there will be (on average) 10,000 bad ones of the same magnitude (as that of the good). This is what Sanford calls “Muller’s Ratchet” (named after another population geneticist). Even if a gene with a beneficial mutation is selected for, it will carry many, many more deleterious ones with it. This inevitably causes genetic entropy, not a complexity increase.
F. While he noted, in chapter 2, that natural selection doesn’t see most of the mutations that occur in the genome (i.e. the “near-neutral” ones which comprise 70-80% of all the bad and 99% of all good), the problem is actually worse due to environmental “noise”. Environmental “noise” is simply the fact that random environmental factors affect who survives to a much greater degree than general fitness. For example, a tree may have greater fitness than that of another tree. However, if the seed of the one with greater fitness lands in a deep valley with little sunlight, and the other lands on a hill that receives proper sunlight, then the one with lesser fitness will survive. In fact, the population geneticist, Kimura (remember: a Darwinist himself), estimates that heritability due to phenotypic superiority (i.e. fitness) is as low as 0.4%! Thus, the “no-selection” box is increased *several* fold, ensuring that the vast majority of all bad mutations will go unnoticed by natural selection, and 99.99% of all beneficial mutations will also go unnoticed. So, while NDET assumes that all (or almost all) selection is due to general fitness, reality says that only about 1/250 of all selection is due to general fitness.
G. While not stated explicitly, NDET presupposes an infinite selection “bank” from which it can assume that all members of a population without a superior genotype can be killed off, leaving only those with superior fitness. [Otherwise, the beneficial mutation would be diluted when it is mingled with the rest of the population.] In reality, however, the selection cost to make a single beneficial mutation (no matter how small) dominant in a population is near extinction! [Sanford cites Kimura who, after doing the math, estimated that each parent in a population must leave about 3.27 million offspring in order to keep up with the selection pressure!] Thus, even if you kill off almost all of a population to keep one beneficial mutation, you will never be able to stop the deterioration of the genome due to the ratio of bad to good mutations and the resultant in-breeding among such a small population. Again, genetic entropy, not increasing complexity, is inevitable.

Chapters 5, 6, 7, and 8
Here, he goes through and refutes various attempts to save NDET from all the problems with it (as mentioned above). Also discussed is the deterioration of the human genome.

Chapter 9
In this chapter, Sanford discusses more of what was discussed in chapters 5-8, but he also throws in several more problems with NDET:

H. NDET assumes that the billions of years (a.k.a. “deep time”) that the earth has been in existence is plenty of time for random mutation and natural selection to give rise to the diversity of life found today. [In my personal experience, I have found that even the mention of “deep time” is enough to dispel any doubts a Darwinist has in his heart about NDET!] However, even assuming that the above problems (A.-G.) don’t exist, the time needed to make only one beneficial nucleotide mutation dominant in a population is *far* too long for even the “deep time” provided. Sanford cites J.B.S. Haldane, another Darwinian geneticist, who calculated that (again, ignoring problems A.-G. above) it would take 300 generations to make a genetic trait fixed in a population. [Note: 300 generations is a conservative number. The average number found in nature is larger than 300.] So, for example, it would take several billion years for a chimp-like ancestor to evolve into a human (again, assuming only beneficial mutations). This famous problem for neo-Darwinism has historically been known as “Haldane’s Dilemma”.
I. NDET assumes that DNA is a linear code, and that one change in a sequence won’t affect other functions in the phenotype. However, recent discoveries have shown that most DNA sequences are “poly-constrained”. That is, DNA sequences can have meanings on several different levels. For example, imagine a coded message that has a valid meaning when read forward, another valid meaning when read backwards, another every 5 letters, and yet another when placed on top of another few messages (making it 3D). This is how most DNA functions, just more complex! Any change in the code could cause an incoherent message, and thus, one good mutation one way might also cause several bad mutations in other ways.
J. Irreducible Complexity. [There has been much debate on this topic, but I agree with Sanford (and Behe for that matter) that direct *and indirect* Darwinian pathways are extremely unlikely (and might as well be impossible). See Behe’s Afterword in the 10th anniversary edition of Darwin’s Black Box.]

Chapter 10
Sanford concludes that the degeneration of the genome is unstoppable and Darwinism could never have gotten off the ground. Contrary to one reviewer’s beliefs about this book, Sanford only spends a few paragraphs on the declining life-spans of the generations of men after Noah. He shows that the life-spans of post-flood man, as recorded in the Bible, follow a curve that is eerily similar to a declining fitness curve found in earlier chapters of this book. In fact, Sanford believes that these recorded life-spans could only have been fabricated if the writer of the Pentateuch (i.e. the 5 books of Moses) used “sophisticated mathematical modeling”. Of course, while this makes Christians (like myself) smile with joy, it probably won’t convince any non-believers.

Everyone who follows this debate should own this book. Even if you are hostile to anyone that even questions NDET, you should read it since college I.D. clubs are handing this book out to their members and, undoubtedly, biology students. My personal opinion is that Sanford gives a devastating critique of neo-Darwinian evolutionary theory. These are insurmountable problems for NDET, and the math and logic prove it. Instead of climbing up Mount Improbable (using Dawkins’ analogy), the genome is tumbling down Mt. Impossible!

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