@sfs I disagree as physicists have moved to quantum mechanics... bosons, quarks, dark matter, but that is not relevant to the topic of this discussion. Thank you for demonstrating that Macroevolution is not being published, as there is no current understanding of the mechanism. That mechanism is inferred, it is not known, therefore not a fact. This is all I have tried to state from the beginning.
In regard to the two papers you linked: I cut and pasted the conclusions and highlighted the key portions. This first paper linked second above, focuses specifically on human gene mapping and "
microevolution", which is indeed proven! Even within the microevolution camp this paper is week as they discovered the alleles were already present and not expressed. This is not a new mutation (highlighted in red below).
"Previous inferences about demographic history and the role of local adaptation in shaping human
genetic variation made from genomewide genotype data4,36,37
have been limited by the partial and complex ascertainment of SNPs on genotyping arrays. Although data from the 1000 Genomes Project pilots are neither fully comprehensive nor fully free of ascertainment bias (issues include low power for rare variants, noise in allele frequency estimates, some false positives, non-random data collection across samples, platforms and populations, and the use of imputed genotypes), they can be used to address key questions about the extent of differentiation among populations, the presence of highly differentiated variants and the ability to fine-map signals of local adaptation. Although the average level of population differentiation is low (at sites genotyped in all populations the mean value ofWright’s Fst is 0.071 between CEU and YRI, 0.083 between YRI and CHB1JPT, and 0.052 between CHB1JPT and CEU), wefind several hundred thousand SNPs with large allele frequency differences in each population comparison (Fig. 5c). As seen in previous studies4,37, the most highly differentiated sites were enriched for non-synonymous variants, indicative of the action of local adaptation. The completeness of common variant discovery in the low-coverage resource enables new perspectives in the search for local adaptation. First, it provides a more comprehensive catalogue of fixed differences between populations, of which there are very few: two between CEU and CHB1JPT (including the A111T missense variant in SLC24A5 (ref. 38) contributing to light skin colour), four between CEU and YRI (including the 246 GATA box null mutation upstream of DARC39, the Duffy O allele leading to Plasmodium vivax malaria resistance) and 72 between CHB1JPT and YRI (including 24 around the exocyst complex component gene EXOC6B); see Supplementary Table 7 for a complete list. Second, it provides new candidates for selected variants, genes and pathways. For example, we identified 139 non-synonymous variants showing large allele frequency differences (at least 0.8) between populations (Supplementary Table 8), including at least two genes involved in meiotic recombination— FANCA (ninth most extreme non-synonymous SNP in CEU versus CHB1JPT) and TEX15 (thirteenth most extreme non-synonymous SNP inCEU versus YRI, and twenty-sixth most extreme non-synonymous SNP in CHB1JPT versus YRI).
Because we are finding almost all common variants in each population, these lists should contain the vast majority of the near fixed differences among these populations. Finally, it improves the fine mapping of selective sweeps (Supplementary Fig. 14) and analysis of the dynamics of location adaptation. For example, we find that the signal of population differentiation around high Fst genic SNPs drops by half within, on average, less than 0.05 cM (typically 30–50 kb; Fig. 5d). Furthermore, 51% of such variants are polymorphic in both populations. These observations indicate that much local adaptation has occurred by selection
acting on existing variation rather than new mutation. The effect of recombination on local sequence evolution We estimated a fine-scale genetic map from the phased low-coverage genotypes. Recombination hotspots were narrower than previously estimated4 (mean hotspot width of 2.3 kb compared to 5.5 kb in HapMap II; Fig. 6a), although, unexpectedly, the estimated average peak recombination rate in hotspots is lower in YRI (13 cM Mb21 ) than in CEU and CHB1JPT (20 cM Mb21 ). In addition, crossover activity is less concentrated in the genome in YRI, with 70% of recombination occurring in 10% of the sequence rather than 80% of the recombination for CEU and CHB1JPT (Fig. 6b). A possible biological basis for these differences is that PRDM9, which binds a DNA motif strongly enriched in hotspots and influences the activity of LD-defined hotspots40–43, shows length variation in its DNA-binding zinc fingers within populations, and substantial differentiation between African and non-African populations, with a greater allelic diversity in Africa43. This could mean greater diversity of hotspot locations within Africa and therefore a less concentrated picture in this data set of recombination and lower usage of LD-defined hotspots (which require evidence in at least two populations and therefore will not reflect hotspots present only in Africa). The low-coverage data also allowed us to address a long-standing debate about whether recombination has any local mutagenic effect. Direct examination of diversity around hotspots defined from LD data are potentially biased (because the detection of hotspots requires variation to be present), but we can, without bias, examine rates of SNP variation and recombination around the PRDM9 binding motif associated with hotspots. Figure 6c shows the local recombination rate and pattern of SNP variation around the motif compared to the same plots around a motif that is a single base difference away. Although the motif is associated with a sharp peak in recombination rate, there is no systematic effect on local rates of SNP variation.
We infer that, although recombination may influence the fate of new mutations, for example through biased gene conversion, there is no evidence that it influences the rate at which new variants appear."
Second paper linked first above: For everyone to read is the highlighted red portions of the second papers conclusions. Please explain how any of this bolsters the pro fact/theory camp? The first highlighted red portion state that 10-40% of the amino acids were adaptive (microevolution) between the 2 genomes. Essentially humans and chimpanzees are more different than previously thought, based on genetics. They tried to state this as politically correct as possible in the first sentence "
This conclusion does not imply that humans have experienced few phenotypic adaptations, or that adaptations have not shaped genomic patterns of diversity." In other words human and chimp phenotype similarities may be coincidental and that the genetic differences we do see for diversity were bigger than previously thought.
"
This conclusion does not imply that humans have experienced few phenotypic adaptations, or that adaptations have not shaped genomic patterns of diversity. Comparisons of diversity and divergence levels at putatively functional versus neutral sites, for example, suggest that 10–15% (and possibly as many as 40% (29)) of amino acid differences between humans and chimpanzees were adaptive (e.g., (30)) as were 5% of substitutions in conserved non-coding regions (22, 29) and ~20% in UTRs (22). Given the paucity of classic sweeps revealed by our findings, an excess of functional divergence would point to the importance of other modes of adaptation. One way to categorize modes of adaptation is in terms of their effect on the allele frequencies at sites that affect the beneficial phenotype. In this view, classic sweeps bring new alleles to fixation; selection on standing variation or on multiple beneficial alleles brings rare or intermediate frequency alleles to fixation; and other forms of adaptation, such as selection on polygenic traits, increase or decrease allele frequencies to a lesser extent.
Such changes in allele frequencies can decrease variation at closely linked sites - to a lesser extent than in a full sweep - and might therefore contribute to a reduction in diversity near functional elements (31), as well as to excess divergence. Alternatives to classic sweeps are likely for parameters applicable to human populations (
7,
32); in particular, many phenotypes of interest are quantitative, and plausibly result from selection at many loci of small effect (
8).
An important implication is that in the search for targets of human adaptation, a change in focus is warranted. To date, selection scans have relied almost entirely on the sweep model, either explicitly by considering strict neutrality as the null hypothesis and a classic sweep as the alternative or implicitly, by ranking regions by a statistic thought to be sensitive to classic sweeps and focusing on the tails of the empirical distribution.
It appears that few adaptations in humans took the form that these approaches are designed to detect, suggesting that low hanging fruits accessible by existing approaches may be largely depleted. Conversely, the more common modes of adaptation likely remain undetected. Thus, in order to dissect the genetic basis of human adaptations and assess what fraction of the genome was affected by positive selection, we need new tests to detect other modes of selection, such as comparisons between closely related populations that have adapted to drastically different environments (e.g., (33)) or methods that consider loci that contribute to the same phenotype jointly (e.g., (34)). Moreover, if alleles that contribute to recent adaptations are often polymorphic within a population, genome-wide association studies should be highly informative.
At least we are looking at the right science!
Regard, GBTG