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Objectives: Mildred Trotter was an anatomist and physical anthropologist whose studies on hair morphology, growth, somatic distribution, and trait relationships to age and ethnogeographic population were foundational to the field of microscopical hair analysis. The collection of human hair samples she assembled for her research has been an underutilized resource for studies on human hair variation. We applied updated methods and reviewed Trotter's original data to reassess the relationship hair traits have to diverse population labels. Methods: Hair form and pigmentation patterns were measured from a subset of the hair samples accumulated by Trotter and we compared our data to Trotter's original results. Variability in hair traits were tested within individuals, within populations, and among ethnogeographic groups. Results: Measured hair cross‐section dimensions and melanosome density and distribution revealed substantial variability within individuals and ethnogeographic populations. Hair traits were found to not be distinctly separable by ancestry but instead showed continuous variation across human populations. Trotter's measurements were precise and the dataset she compiled remains valid, though the conclusions should be reviewed in light of our current understanding of human variation. Discussion: Our findings support moving away from categorical ancestry classifications and eliminating the use of outdated racial typologies in favor of more descriptive trait analysis. Detailed analysis of trait pattern distributions are presented that may be useful for future research on human variation. We point to the need for additional research on human variation and hair trait relationships with reference to known population affinity. [ABSTRACT FROM AUTHOR]

SNP heritability h s n p 2 is defined as the proportion of phenotypic variance explained by genotyped SNPs and is believed to be a lower bound of heritability ( h 2 ), being equal to it if all causal variants are known. Despite the simple intuition behind h s n p 2 , its interpretation and equivalence to h 2 is unclear, particularly in the presence of population structure and assortative mating. It is well known that population structure can lead to inflation in h ˆ s n p 2 estimates because of confounding due to linkage disequilibrium (LD) or shared environment. Here we use analytical theory and simulations to demonstrate that h s n p 2 estimates can be biased in admixed populations, even in the absence of confounding and even if all causal variants are known. This is because admixture generates LD, which contributes to the genetic variance, and therefore to heritability. Genome-wide restricted maximum likelihood (GREML) does not capture this contribution leading to under- or over-estimates of h s n p 2 relative to h 2 , depending on the genetic architecture. In contrast, Haseman-Elston (HE) regression exaggerates the LD contribution leading to biases in the opposite direction. For the same reason, GREML and HE estimates of local ancestry heritability h γ 2 are also biased. We describe this bias in h ˆ s n p 2 and h ˆ γ 2 as a function of admixture history and the genetic architecture of the trait and show that it can be recovered under some conditions. We clarify the interpretation of h ˆ s n p 2 in admixed populations and discuss its implication for genome-wide association studies and polygenic prediction.