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Genetics, Vol. 173, 953-964, June 2006, Copyright © 2006
doi:10.1534/genetics.105.054312
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* Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853, Plant Genome Mapping Laboratory, University of Georgia, Athens, Georgia 30602 and Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853
2 Corresponding author: IGD, 156 Biotechnology Bldg., Cornell University, Ithaca, NY 14853.
E-mail: mth3{at}cornell.edu
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ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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Domesticated species are a special case of derived populations: their population genetic characteristics are a complicated product of the characteristics of the ancestral population modified by demographic events, such as bottlenecks, migration, and nonrandom mating, and by varying degrees of selection on genes underlying traits important to farmers and breeders. Selection during domestication may target alleles that are neutral in the wild ancestor and are segregating at moderate frequencies; in such cases, predictions of simple models of selection on new mutations will not be met (ORR and BETANCOURT 2001; INNAN and KIM 2004; PRZEWORSKI et al. 2005). Domesticated plants may also experience introgression to/from wild relatives due to the lack of reproductive isolation between wild ancestors and their very recently derived (and abundant) cultivated descendants, resulting in cultivated individuals that carry wild alleles and outgroup taxa that carry cultivated alleles. Successful use of genomewide scans to identify domestication genes has been largely limited to maize, where patterns of variation at both SSRs and SNPs have revealed loci that appear to have lost more variation than can be accounted for by the domestication bottleneck (VIGOUROUX et al. 2002; TENAILLON et al. 2004; WRIGHT et al. 2005; YAMASAKI et al. 2005).
We are studying the population genetics of the cultivated tropical grass Sorghum bicolor, which was most probably domesticated in eastern Africa 3000–6000 years ago, subsequently spread to the entire African continent, and reached Asia during the first millennium (KIMBER 2000). Much more recently (mid-nineteenth century), sorghum was brought to the United States. There is a great deal of morphological diversity in cultivated sorghum, in characters such as seed size and color, panicle architecture, and height. Furthermore, because sorghum is grown in environments that differ dramatically from one another (e.g., in rainfall, temperature, soil type, and elevation), there must be physiological diversity as well (DOGGETT 1988). We are interested in characterizing the patterns of genetic variation in worldwide samples of grain sorghum, with the goal of identifying loci that are important in domestication, local adaptation, and agronomic performance. In a previous study of genomewide patterns of sequence variation (HAMBLIN et al. 2004), 95 loci of average length 275 bp were surveyed. In this study, we resequenced 204 additional loci of average length 671 bp distributed throughout the genome in a sample of 16 cultivated grain sorghum landraces (i.e., not elite, modern cultivars) as well as the reference elite cultivar BTx623 and an outgroup, S. propinquum. We characterize the overall patterns of sequence variation, compare those patterns to the predictions of some simple bottleneck models, and test for evidence of both directional and diversifying selection.
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MATERIALS AND METHODS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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When possible, we extended the length of the original DNA sequence of each locus through iterative searches against the GenBank GSS and EST databases. Primers were designed from these contigs so that in some cases primers amplified regions flanking the original locus. Loci that could be successfully amplified from BTx623 and S. propinquum DNA were then amplified from the panel of 16 sorghum accessions.
Sequencing and analysis:
Total genomic DNA was isolated from individual seedlings following a standard CTAB extraction protocol (DOYLE and DOYLE 1987) and used as template in PCRs following previously established protocols (CASA et al. 2005). PCR products were prepared for direct sequencing by treatment with exonuclease I (New England Biolabs) and shrimp alkaline phosphatase (Promega) following the manufacturers' instructions. Single-pass sequencing was performed at the BioResource Center (Cornell University) using a single PCR primer, as most individuals were homozygous. Double-pass sequences were obtained only when putative heterozygotes were observed. Chromatograms were assembled into contigs using Sequencher (Gene Codes, Ann Arbor, MI) software. Alignments were then visually inspected and manually edited. Each set of sequence chromatograms was inspected independently by two or three people.
Summary statistics of diversity and divergence were obtained with DnaSP version 4.0 (ROZAS et al. 2003). Blocks of three or more contiguous SNPs were disregarded. Insertion/deletion polymorphisms were not considered in either the diversity or divergence analyses. Loci were tested for departure from neutrality using the method of HUDSON et al. (1987) implemented by the multilocus HKA software (Jody Hey, available at http://lifesci.rutgers.edu/
heylab/index.html). One locus, pSB1812, was not tested with all 203 other loci because it caused some sort of (unknown) incompatibility in the simulations. When tested with a subset of the data, this locus showed no unusual pattern of polymorphism and divergence. Significance of individual loci was assessed by removing the most significant locus and testing the remaining n – 1 loci iteratively until no significant locus was detected. The HKA program was also used to test for significance of average D (TAJIMA 1989) under the standard neutral model and to estimate 
(4Neµ) on the basis of both polymorphism and divergence.
Assignment of coding regions:
To determine whether sequenced regions corresponded to protein-coding loci, consensus sequences obtained from aligned loci were used in database searches (blastn and blastx) using GenBank default parameters. For classifying a region as an open reading frame, we used the criteria described by HAMBLIN et al. (2004).
Simulations:
Models of population history were simulated using the program ms (HUDSON 2002) with the following assumptions: (1) ancestral (4Neµ) is the same as in the wild population today and is estimated to be 0.0057/bp on the basis of variation at 24 loci in 4–26 accessions of S. bicolor ssp. verticilliflorum (A. ZAMORA and our unpublished data); (2) at individual loci as estimated by the program HKA (see above) was scaled relative to the ancestral ; (3) ancestral Ne is calculated as follows: 4Neµ = 0.0057; therefore Ne = 0.0057/4/1 x 10–8 = 1.43 x 105, where a neutral mutation rate of 1 x 10–8/bp/generation is based on sequence divergence at 11 loci between maize and sorghum (SWIGONOVA et al. 2004); (4) the time of domestication is assumed to be in the range of 3000–6000 years ago, on the basis of archeological evidence, roughly equivalent to 0.005–0.01 when scaled in 4Ne generations, at 1 generation/year; and (5) 4Ner is estimated as 4 x (1.43 x 105) x (4 x 10–8 crossovers/bp/generation) x locus length x 0.46, where 0.46 = (1 – F) to account for a self-pollination rate of 0.7 (HAMBLIN et al. 2005). A recent analysis of genetic vs. physical distance on a chromosomal scale came to an identical estimate of average r (0.25 Mb/cM) for euchromatic regions (KIM et al. 2005). We simulated a small set of models where all parameters were fixed except for the size of the bottlenecked population, which was fit to the observed value of average S, and tested whether other summary statistics produced by the model were consistent with the data.
Tests of haplotype number:
The probability of observing K haplotypes given an observed number of segregating sites, S, was obtained using the program haploconfig (INNAN et al. 2005). This program generates gene genealogies on the basis of an input value (or range) of and accepts only those that have the observed number of segregating sites. Using the overall average value of for this data set (1.5), large numbers of segregating sites are very unlikely to be observed and the acceptance rate becomes unreasonably low. We therefore performed simulations where was chosen to maximize the acceptance rate. For S < 18, the acceptance rate was 
5%, but for large values of , the probability of observing any particular value of S becomes quite small even when is optimized. These simulations were performed (1) without recombination, which is very conservative, and (2) with 4Ner = 5, which is slightly conservative on the basis of empirical estimates of crossing over (see Simulations).
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RESULTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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Patterns of sequence diversity across the genome:
Approximately 138 kb of DNA sequence (alignment gaps excluded) were surveyed per individual. Forty-three loci (21%) were invariant within cultivated S. bicolor. One of these loci was also invariant across species. Levels of polymorphism and divergence at individual loci are presented as supplemental data (Table S1 at http://www.genetics.org/supplemental/). Table 2 presents summary statistics from this study as well as those from our previous study (HAMBLIN et al. 2004). Levels of nucleotide diversity and divergence are consistent with the previous estimates based on a different set of loci in a different species-wide sample that was also chosen to capture racial and geographic diversity, but without prior knowledge of genetic relationships.
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95%), length polymorphisms were short (<20 bases). This proportion is similar to that observed in maize, where 92% of nonmicrosatellite indels were <20 bp in length (TENAILLON et al. 2002). DNA secondary structure prediction programs and BLAST searches revealed that 4 of the 13 indels >20 bp contained insertions similar to miniature inverted repeat transposable elements (MITEs). When queried against the EST database, these putative transposable elements had matches to sorghum EST sequences derived mostly from stress-induced libraries (e.g., wound, salt, and heat shock).
Consensus DNA sequence from each locus was used in BLAST searches (see MATERIALS AND METHODS) to identify exons and introns for analysis by functional category. Results from this partitioning are presented in Table 3. Across loci, the numbers of synonymous and nonsynonymous changes within vs. between species showed no departure from the neutral equilibrium expectation (MCDONALD and KREITMAN 1991) (Table 4). This result contrasts with that of our earlier, smaller study, which found a significant excess of replacement polymorphism (HAMBLIN et al. 2004). Diversity levels in introns were lower (
= 0.24%) than at synonymous sites ( = 0.38%), consistent with observations in other species.
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as well as the rate of recombination, so it is most easily estimated by simulations (DEPAULIS and VEUILLE 1998). For all loci with S 
5, we tested whether the number of haplotypes was unusual, using simulations based on and conditioned on S (see MATERIALS AND METHODS). These simulations showed that a large fraction of loci had significantly fewer haplotypes than expected (Table 6). Even under a very conservative (and unrealistic) assumption of no recombination, 30% of tested loci had a significantly low haplotype number. These results are consistent with a previous observation that linkage disequilibrium (LD) in sorghum is more extensive than expected under assumptions of equilibrium (HAMBLIN et al. 2005).
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30%, so some of the very low H values in our data set may be evidence of selection. However, as PRZEWORSKI (2002) has shown, a significant H statistic is "not a unique signature of positive selection." Population structure can give rise to an excess of significant H values, as could introgression from a divergent population. Therefore, in the absence of independent evidence, these results must be interpreted with caution. Neutral processes likely explain most of the extreme values of H observed in this data set.
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varies among loci. However, it is likely that a bottleneck will inflate the variance in ratios of polymorphism to divergence, making the test anticonservative (e.g., HAMMER et al. 2004; HADDRILL et al. 2005). These results, therefore, should only be used to identify outliers, rather than as a test of significance. Of the 5% of loci that contributed the most to the HKA statistic, only one (PRC0378) showed a deficiency of polymorphism relative to divergence (Table 8), the expected signature of directional selection.
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The sequence for PRC0378 was obtained from an EST library of rhizome cDNAs from S. halepense and maps to a QTL for rhizome traits in a cross between S. bicolor and S. propinquum (HU et al. 2003). The QTL extends over a large region of the sorghum map, however, so this could easily be coincidence. Since neither wild nor cultivated S. bicolor has rhizomes, underground stems involved in clonal propagation and associated with perenniality, a selective sweep associated with a rhizome trait would likely have occurred in the ancestral population rather than during domestication.
As for the six loci that appear to have an excess of variation, three are among the loci that are highlighted in Table 7 because of their excess of singletons on a few lineages, and the fourth, pHER-1E07, shares this pattern. It is the large number of singletons, rather than a signature characteristic of diversifying selection, that has produced the significant test statistic at those loci. The two remaining loci, pSB0643 and pSB1804, have positive D values and gene genealogies that are more consistent with diversifying selection. Locus pSB0643 is closely linked to Dw2, a locus associated with variation in plant height that is expected to show local adaptation. Variation at pSB0643 is distributed in two clades of 7 and 10 individuals with 12 fixed differences between them (Figure 1a); there is no obvious geographic or phenotypic structure to the groups (see Table 1). Variation at locus pSB1804, which has homology to a transport-like protein in rice (XP_466724), is also distributed in two clades (Figure 1b). There are 16 fixed differences, including 9 amino acid differences, between them; 2 unique haplotypes do not fall into either clade. Again, there is no obvious geographic or phenotypic structure to the variation.
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We used a T0 of 0.005 as a starting point and increased it incrementally to assess the fit of the resulting summary statistics (Table 5). For all models tested, the variances of the summary statistics were larger and H was more negative. However, models that were consistent with the estimated time of the bottleneck, i.e., T0 = 0.005–0.010, had average D values much larger than we observed (BN1 and BN2 in Table 5). Only when the time since the bottleneck was increased to 0.025, arguably equivalent to 14,000 years ago, did D approach the observed value (BN5). Conversely, BN1 and BN2 produced average values of H that were closer to the observed data, but the fit to H was worse in BN5.
The median values produced by these models (Table 5) show that none of these models is a good fit to the data, but they do not tell us whether the models exclude the data. Because the variances are large, any given iteration of a particular model might, by chance, produce a result that is much closer to the observed data. Indeed, Figure 2 shows that all five bottleneck models could produce the observed variance in S, variance in D, average H, and variance of H, although these values are in some cases in the tails of the distributions. However, for the recent bottlenecks, the range of average D (in 100 simulations) does not include the observed value.
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DISCUSSION |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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The time of a domestication bottleneck:
The statistic that varied the most among models was Tajima's D (Figure 2). A bottleneck 0.005 or 0.010 4Ne generations ago, our best estimate based on independent data (see MATERIALS AND METHODS), produces average D values that are strongly positive due to the loss of rare alleles. Models that place the bottleneck further in the past produced a better fit to the data (implying that our estimate of ancestral Ne is too large), in which D is very slightly negative. This is because additional time since the bottleneck allows for the accumulation of new, rare variants at loci where variation had been eliminated (a star-shaped genealogy). Thus there are more strongly negative and more strongly positive D's than expected under neutrality, producing a large variance in D, but an average near zero, as we observe. The strongly negative D's in our data set, however, are not produced by star-shaped genealogies (see RESULTS and Table 7), suggesting that this scenario is not appropriate and that fitting a model to summary statistics can be misleading. Furthermore, the fit to H, the variance of H, and the variance of S are all marginal (see Figure 2).
To reconcile an older bottleneck (i.e., T0 = 0.025) with a domestication time of 6000 years ago would require either that our estimate of µ be twofold larger or that our estimate of ancestral 4Neµ/bp (A) be twofold smaller. Thus some combination of higher µ, smaller A, and older time of domestication may be able to explain the data. Our estimate of A may, in fact, be somewhat inflated, if our sample of wild accessions includes individuals from subpopulations that did not contribute to the cultivated population. However, a twofold smaller A would be very similar to 4Neµ in cultivated sorghum, inconsistent with a bottleneck.
Another piece of evidence to be considered is that a different species-wide sorghum sample produced a sequence data set with a positive average value of D (0.29; see Table 2). This positive value is consistent with a more recent bottleneck and with the other independent data. For this reason, we suggest that a more recent bottleneck is more likely correct for cultivated sorghum, but that the true evolutionary model includes other factors (e.g., population structure and migration) that generate a sufficiently large variance that both positive and near-zero average D's can be observed when different loci, or different individuals, are sampled. Some models that include population structure and migration do have sufficiently large variances, although they are a poor fit to other aspects of the data (not shown).
Sampling issues:
The discrepancy in average Tajima's D, as well as a difference in the ratio of nonsynonymous to synonymous polymorphism, in different samples raises some issues about sampling (see Tables 2 and 4). Both of these samples were intended to be representative of species-wide diversity in S. bicolor, but were chosen using slightly different criteria. The HAMBLIN et al. (2004) sample (n = 22 accessions) was chosen to maximize geographic distribution and morphological variation, as no genetic data were available at that time. Criteria for the sample in the current study (n = 17 accessions) also included maximization of genetic diversity, as assessed by variation at 74 SSR loci (CASA et al. 2005); it contains 8 accessions in common with the 2004 study.
Independent samples chosen randomly from a true single population should have similar properties, i.e., the lineages should be exchangeable; the fact that they are not suggests that all members of the population do not share the same history. In this case, the sample was not drawn from a geographically restricted locality but instead is scattered, a sampling technique that is appropriate when species-wide variation is of interest and when natural populations do not exist. When each individual comes from a different deme, so that only the collecting phase of the genealogy (coalescence and migration among demes) is captured by the sample, the properties of the sample should be similar to those of a panmictic population (WAKELEY 2004). In our data, it appears that the chance sampling of more divergent haplotypes has contributed enough low frequency variants to bring the average D close to zero even though another sample showed the expected effect of a recent bottleneck, namely a positive average D. These divergent haplotypes could be due to population structure in the ancestral and/or current population, in a scenario that violates the assumptions of Wakeley's analysis. We may have inadvertently increased the probability of sampling divergent lineages in this study, since we maximized genetic distance in selecting the sample. Given the star-shaped sorghum genealogy based on variation at SSRs (see Figure 1 in CASA et al. 2005), we did not expect this effect; however, the Casa et al. study may have been too small to detect structure. Early studies based on small numbers of individuals and markers concluded that Arabidopsis has no population structure, but later studies have come to quite different conclusions (NORDBORG et al. 2005; SCHMID et al. 2006). Unlike NORDBORG et al. (2005), we are unable to attribute the divergent haplotypes to one or two lineages; many accessions contributed divergent haplotypes at different loci.
With regard to the discrepancy in the ratio of nonsynonymous to synonymous polymorphism, the difference is due to the number of replacement variants detected, as the level of synonymous polymorphism is very similar in the two studies. This could be a consequence of sampling but could also be due to sample size if amino acid polymorphism is slightly deleterious, since some fraction of low frequency amino acid variants that would be observed in a sample of 22 would be missed in a sample of 16.
Lack of evidence of directional selection:
Because of sorghum's history of domestication, which was very recent in evolutionary terms, we expected to see evidence of directional selection at some loci but found none when we used the multilocus HKA test (Table 8). Strong evidence of directional selection was also lacking in two previous studies of genomewide variation in S. bicolor (CASA et al. 2005; HAMBLIN et al. 2004). Considering the three studies together, a total of 445 loci (371 sequenced loci totalling167 kb, plus 74 SSR loci) have been surveyed. While the loci surveyed had been genetically mapped, they were mapped in a cross between S. bicolor and S. propinquum and thus were not biased to be variable within S. bicolor.
If many loci in the data set have experienced selection, the multilocus HKA test may be overly conservative because the overall distribution is non-neutral. To assess the effect that this might have had on our results, we also performed HKA tests using pooled data from a putatively neutral reference subset of 22 loci chosen by the following criteria: the loci had (1) a small (<0.5) deviation in the HKA test of all loci and (2) a |D| statistic of <0.5. In tests of each locus vs. the pooled data, 42 were significant at the 0.05 level: 23 loci with a deficiency of variation and 19 loci with an excess of variation. This far exceeds the 10 loci expected by chance, suggesting that some of these loci are true outliers. Conversely, none of the loci met the Bonferroni-corrected significance criterion of P < 0.05/204 = 0.0002, so this procedure does not change our conclusions.
Comparisons with maize:
In cultivated maize, WRIGHT et al. (2005) estimated that a minimum of 2–4% of the genome has experienced directional selection. If the proportion in sorghum were similar, and the probability that any random locus has experienced directional selection were 0.02, then the chance that 445 randomly chosen loci include zero selected loci would be quite small (0.00012). Furthermore, because of more extensive LD in sorghum (HAMBLIN et al. 2005), we expect the footprint of selection to be more extensive, increasing the percentage of the genome affected. There are a number of possible factors that might explain the difference between our results and those obtained for maize. Some of those factors have to do with the biology and history of the organism, while others have to do with the methodological approaches used to study them.
The simplest and most obvious explanation for our negative results is that low variation in sorghum provides poor power for detecting directional selection and that much larger regions need to be sequenced to obtain sufficient power. While the average size of regions sequenced in this study is twice the average size surveyed in WRIGHT et al. (2005), the average number of segregating sites per locus is roughly one-fourth. This implies that genomewide scans in organisms with low variation may require at least several kilobases of sequence at each locus. Sorghum and maize also have a major difference in mating system: sorghum's high frequency of self-pollination reduces the effective rate of recombination, which increases linkage disequilibrium and likely contributes to the very large variances associated with summary statistics. Finally, the domestication process in sorghum and maize may have been quite different. While maize has undergone a very dramatic change in plant growth habit and ear morphology compared to the progenitor teosinte (DOEBLEY and STEC 1993), the morphological changes in domesticated sorghum are more subtle and fewer major genes may be involved. Selection in sorghum may have acted more frequently on standing variation than on new or rare mutations; in such cases, the signal of directional selection may be weak (ORR and BETANCOURT 2001; INNAN and KIM 2004; PRZEWORSKI et al. 2005). Clear signals of selection may also have been obscured if domestication spread through a structured population, producing patterns of neutral variation very different from those expected following selection in a panmictic population (SLATKIN and WIEHE 1998; SANTIAGO and CABALLERO 2005).
Tests for directional selection in maize have largely been based on simulations that model the bottleneck from teosinte to maize (VIGOUROUX et al. 2002; TENAILLON et al. 2004; WRIGHT et al. 2005), while our approach in this study was to look for a deficiency of variation relative to divergence to the only available sequence of an outgroup, S. propinquum, which is not the direct ancestor of cultivated sorghum. In cases where maize genes have been tested by both methods, it has been suggested that the comparison between maize and teosinte produces more significant results. TENAILLON et al. (2004), for example, concluded that "the addition of parviglumis [teosinte] data has improved significantly the ability to infer selection," when they found strong evidence of selection on ts2 and d8, two genes for which the HKA test had produced equivocal results. In a recent article (YAMASAKI et al. 2005), both methods were used to test for evidence of selection during domestication, and the difference in results was small; using the simulations, six loci showed significant evidence for selection, while five were significant by the HKA test. The difference was greater for loci showing evidence of improvement (i.e., selection in inbreds only). Whether this difference represents increased power or a higher false-positive rate is not known.
Comparisons with Drosophila:
Detecting evidence of adaptation in the context of a population bottleneck is a problem also faced in population genetic studies of Drosophila, as non-African (i.e., derived) populations show genomewide departures from equilibrium and reduced variation relative to African populations. In some cases, a bottleneck model can explain most of the reduction in variation in non-African populations (KAUER et al. 2003; HADDRILL et al. 2005; THORNTON and ANDOLFATTO 2006). While multilocus patterns are often suggestive of adaptive evolution in derived populations (ORENGO and AGUADE 2004; SCHOFL and SCHLOTTERER 2004), it has been hard to demonstrate that selection has acted on a particular locus (but see BAUER DUMONT and AQUADRO 2005; CATANIA and SCHLOTTERER 2005 for exceptions). Furthermore, apparent signals of selection in non-African populations often appear, upon further study, to be amplified signals of selection in the ancestral African populations (LI and STEPHAN 2005; BEISSWANGER et al. 2006; POOL et al. 2006), rather than evidence of adaptation to temperate environments. Interestingly, OMETTO et al. (2005) found that a large proportion (>60%) of unusual loci in a non-African sample of Drosophila had an excess, rather than a deficiency, of polymorphism. As in our case, this may reflect increased power for detecting diversifying selection in a population with overall low levels of variation. Thus the difficulty of detecting directional selection in a bottlenecked population is not unique to domesticated species and presents important challenges for the field of population genetics.
Conclusions:
Detecting a locus-specific reduction in variation, diagnostic of an episode of directional selection, is difficult when levels of variation at neutral loci are already low. When low levels of variation are caused by a bottleneck, they are accompanied by perturbations of the frequency spectrum and increased LD that further decrease the signal-to-noise ratio. In the case of S. bicolor it appears that, in addition to a bottleneck, other population-level phenomena have contributed to the observed departures from equilibrium. We base this conclusion on the observation that simple bottleneck models are inconsistent with the data and that, while several loci had extreme negative values of Tajima's D statistic, in not one case was this due to a star-like genealogy. Other phenomena that might underlie this observation could be, for example, ancestral population structure, multiple domestications, or introgression from wild conspecifics or congeners. More sophisticated models (incorporating data from wild populations) may, in principle, allow disentanglement of these factors. Phenotypic analyses in experimental populations, however, complementing population genetic analyses, may prove to be a more fruitful strategy for elucidating the genetic basis of the cultivated phenotype (WRIGHT and GAUT 2005).
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ACKNOWLEDGEMENTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSLITERATURE CITED |
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FOOTNOTES |
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1 These authors contributed equally to this work. 
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LITERATURE CITED |
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