\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Haplotyping interspecific hybrids by dual alignment to both parental
genomes}



\author[1]{Patrick Brown}%
\affil[1]{University of California Davis}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Sequencing-based genotyping of heterozygous diploids requires sufficient
depth to accurately call heterozygous genotypes. In interspecific
hybrids, alignment of reads to both parental genomes simultaneously can
generate haploid data, potentially eliminating the problem of
heterozygosity. Two populations of interspecific hybrid rootstocks of
walnut (Juglans) and pistachio (Pistacia) were genotyped using alignment
to the maternal genome, paternal genome, and dual alignment to both
genomes simultaneously. Downsampling was used to examine concordance of
imputed genotype calls as a function of sequencing depth. Dual alignment
resulted in datasets essentially free of heterozygous genotypes,
simplifying the identification and removal of cross-contaminated
samples. Concordance between full and downsampled genotype calls was
always highest after dual alignment. Nearly all SNPs in dual alignment
datasets were shared with the corresponding single-parent datasets, but
60-90\% of single-parent SNPs were private to that dataset. Private SNPs
in single-parent datasets had higher rates of heterozygosity, lower
levels of concordance, and were enriched in fixed differences between
parental genomes (``homeo-SNPs'') compared to shared SNPs in the same
dataset. In multi-parental walnut hybrids, the paternal-aligned dataset
was ineffective at resolving population structure in the maternal
parent. Overall, the dual alignment strategy effectively produced
phased, haploid data, increasing data quality and reducing cost.%
\end{abstract}%



\sloppy


\textbf{Introduction}

The prodigious throughput of short-read sequencing technology has
revolutionized quantitative genetics by allowing multiplexed genome-wide
genotyping of large numbers of individuals with minimal ascertainment
bias (Davey et al., 2011; Andrews et al., 2016). A major technical
challenge to this approach is accurate calling of heterozygous genotypes
at low sequencing depth. To circumvent this problem,
reduced-representation libraries are often generated using restriction
enzymes (Baird et al., 2008; Elshire et al., 2011) or sequence capture
(Gnirke et al., 2009; Ali et al., 2016), increasing sequencing depth
across a subset of the genome. However, haploid or inbred individuals
can generally be genotyped and imputed much more accurately and
inexpensively than heterozygous individuals (Swarts et al., 2014). A
second challenge to genotyping with low-depth short-read data is the
possibility of ``homeo-SNPs'' arising from alignment of reads from
homeologous regions of the genome (Tinker et al., 2014; Hulse-Kemp et
al., 2015). These false polymorphisms can often be identified by their
excess heterozygosity relative to Hardy-Weinberg equilibrium, but
homeo-SNPs that escape filtering may interfere with imputation and
estimation of relatedness between individuals. Homeo-SNPs are
particularly problematic in polyploids and interspecific hybrids.

Polyploidy and interspecific hybridization are common features of plant
evolution that are exploited in plant breeding to generate novelty,
increase vigor, and stack desirable alleles from different species (Alix
et al., 2017). Tree and vine crops often rely on interspecific hybrid
rootstocks to increase vigor and resilience to biotic or abiotic
stresses without affecting fruit or nut quality in the grafted scion
(Warschefsky et al., 2015). In California, for example, production of
almonds \emph{(Prunus dulcis)} (Ledbetter and Sisterson, 2008),
walnuts\emph{(Juglans regia)} (Ramasamy et al., 2021), and
pistachios\emph{(Pistacia vera)} (Ferguson et al., 2002) relies on
rootstocks that are interspecific hybrids. Each of these nut crops has a
mating system that can be exploited to generate large numbers of hybrid
progeny (self-incompatibility, monoecy, and dioecy, respectively), and
superior hybrid genotypes can be propagated clonally. However, genetic
gain in tree breeding programs is generally slow due to the time and
space required for evaluation, as well as the difficulty of genotyping
highly heterozygous material.

This study evaluates different methods for generating genotype data from
elite populations of interspecific hybrid pistachio (\emph{P. atlantica
X P. integerrima} ; n=725) and walnut (\emph{J. microcarpa X J. regia} ;
n=228) rootstocks. Short read sequencing was performed on
reduced-representation libraries for each species. A typical workflow
would be to align the resulting reads against either the maternal (P1)
or paternal (P2) genome (Figure 1). Because interspecific hybrids are
composed of one haploid gamete from each parent, we expected that
alignment to both parental genomes simultaneously (P1+P2) would result
in haploid data, avoiding depth thresholding and greatly increasing
genotyping efficiency for heterozygous germplasm.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Materials and Methods}

\emph{Plant material}

Pistachio interspecific hybrid rootstocks consisted of 768 seedlings
derived from a single cross of \emph{P. atlantica X P.
integerrima}provided by Foundation Plant Services (Davis, CA) and were
sequenced in 2 Illumina HiSeq SR100 lanes at the UC Davis Genome Center.
Walnut interspecific hybrid rootstocks consisted of 228 clones derived
from three crosses (\emph{J. microcarpa} individuals 31.01, 31.09. and
29.11 crossed with pollen from \emph{J. regia} cv ``Serr'') (Ramasamy et
al., 2021) and were sequenced in a single Illumina NextSeqHigh 75 lane.

\emph{Genotyping, imputation, and downsampling}

GBS libraries were prepared as previously described (Poland et al.,
2012) using simultaneous restriction-ligation with HindIII-HF, MseI, and
T4 DNA Ligase (NEB). Following the TASSEL GBS pipelineV2 (Glaubitz et
al., 2014), BWA (Li and Durbin, 2009) was used to align 64 bp tags to
reference assemblies for either the maternal parent species (P1), the
paternal parent species (P2), or both maternal and paternal reference
assemblies simultaneously (P1+P2). Reference assemblies for pistachio
species were obtained from Palmer et al. (2022), for \emph{J.
microcarpa} from Zhu et al. (2019), and for \emph{J. regia} from Marrano
et al. (2020). Only tags that aligned uniquely (MAPQ\textgreater{}=20)
were retained. The SNPQualityProfilerPlugin in TASSEL was used to remove
candidate SNPs with low depth (log(depth)\textless{} -1) and low
inbreeding coefficient (F \textless{} -0.05 for P1 and P2 alignments; F
\textless{} 0.9 for P1+P2 alignments) before SNP calling. Vcftools
(Danecek et al., 2011) was used to remove taxa with \textgreater{}90\%
missing data, and for depth thresholding (--minDP 5) of P1 and P2
datasets only. Imputation with Beagle 5.4 (Browning et al., 2018) was
performed with no reference panel and a window size and walk speed of 12
and 4 Mb respectively. Downsampling (50\%) was performed using the
reformat.sh command in bbmap (Bushnell, 2014).

\textbf{Results and Discussion}

\emph{Effects of alignment strategy on heterozygosity and minor allele
frequency}

Three genotypic datasets each were generated for both \emph{Pistacia}and
\emph{Juglans} , by aligning reads to the maternal genome only (P1), to
the paternal genome only (P2), or to both maternal and paternal genomes
simultaneously (P1+P2). Dual alignment to both parental genomes resulted
in a higher proportion of reads aligning for hybrids of both genera
(Table 1). SNPs with low coverage and excess heterozygosity were
filtered from each dataset, and a depth threshold of 5 was applied to P1
and P2 datasets prior to imputation to minimize undercalling of
heterozygotes. Results are summarized in Table 1. Note
that\emph{Juglans} and \emph{Pistacia} have basic chromosome numbers of
16 and 15, and that dual alignment (P1+P2) results in alignment to 32
and 30 chromosomes respectively.

\textbf{Table 1. Summary of \emph{Juglans} and \emph{Pistacia} datasets
produced using three different alignment strategies.}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Genus\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Alignment strategy (\% aligned)\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Down-sampling\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Taxa\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
SNPs\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Chromo-somes\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Avg. MAF\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Heterozygote Frequency\strut
\end{minipage} & \begin{minipage}[b]{0.11\columnwidth}\raggedright\strut
Data retained after depth thresholding (\%)\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{Juglans}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{microcarpa} (P1; 63\%)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
none\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
228\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
114965\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.163\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.066\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
18.3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
50\%\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
226\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
113380\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.147\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.048\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
10.2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\emph{Regia} (P2; 55\%)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
none\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
228\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
83355\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.191\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.093\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
16.4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
50\%\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
228\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
83515\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.168\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.064\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
10.7\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
Dual (P1+ P2; 76\%)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
none\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
224\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
65517\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
32\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.29\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.003\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
100\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
50\%\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
222\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
64607\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
32\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.28\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.003\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
100\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{Pistacia}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{atlantica} (P1; 60\%)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
none\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
731\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
33602\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.274\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.071\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
9.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
50\%\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
714\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
23984\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.265\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.05\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
4.8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{integerrima} (P2; 57\%)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
none\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
731\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
30202\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.22\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.094\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
10.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
50\%\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
714\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
21099\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.205\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.067\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
7.7\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Dual (P1+P2; 65\%)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
none\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
725\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
13361\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.468\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.002\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
100\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
50\%\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
725\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
11642\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.46\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.002\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
100\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Heterozygote frequencies in \emph{Juglans} and \emph{Pistacia} P1+P2
datasets are 0.003 and 0.002 respectively, 20-50X lower than in their
corresponding P1 and P2 datasets (Table 1). The proportion of
heterozygous genotypes in P1 and P2 datasets increases strongly with
increasing read depth, whereas the proportion in P1+P2 datasets is not
affected by read depth (Figure 2). Most heterozygous genotypes in P1+P2
datasets are concentrated in a few individuals, probably due to
cross-contamination during DNA extraction or library preparation (Figure
2). Therefore, the dual alignment strategy for interspecific hybrids
results in effectively haploid datasets and enables simple detection and
removal of cross-contaminated samples.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Average minor allele frequency (MAF) in P1 and P2 datasets is
approximately half of that in the corresponding P1+P2 datasets (Table
1). In the \emph{Pistacia} P1+P2 dataset derived from a single cross,
almost all SNPs have MAF values close to 0.5. MAF frequencies in
the\emph{Juglans} dataset are more variable since this population is
derived from three families. Two of these families are at frequencies
close to 0.5, and the third is at less than 0.05 frequency. In
the\emph{Juglans} P1+P2 dataset, three distinct peaks in the MAF
distribution are observed close to 0.5, 0.25, and 0.05. The abundance of
SNPs with MAF \textasciitilde{}0.125 in P1 and P2 (but not P1+P2)
datasets likely indicates segregation of diploid SNPs in one of the two
more frequent Juglans crosses.

\emph{Concordance between genotypes before and after downsampling}

To assess the robustness of each alignment strategy to reduced
sequencing depth, raw Illumina files were downsampled by 50\%, and
genotype calls for each taxon from the imputed, downsampled datasets
were compared to genotype calls from the full imputed datasets.
Concordance between full and downsampled datasets is much higher using
dual alignment (P1+P2; Figure 3). Downsampling also results in a
one-third reduction in heterozygosity in P1 and P2 datasets but does not
affect the proportion of heterozygous genotypes in P1+P2 datasets (Table
1). With increasing sequencing depth, concordance in P1+P2 datasets
increases as expected, whereas concordance in P1 and P2 datasets is
unexpectedly higher at low depth than at intermediate sequencing depth.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\emph{Shared and private SNPs across datasets}

Virtually all the SNPs in the P1+P2 dataset for each genus are shared
with either the corresponding P1 or P2 dataset (Figure 4). However, the
much larger P1 and P2 datasets are composed primarily (60-90\%) of
private SNPs that are not present in the P1+P2 dataset. To investigate
the hypothesis that these private SNPs are enriched with homeo-SNPs, we
compared SAM files resulting from alignment of unique reads to P1, P2,
and P1+P2 genomes, and associated each SNP with its underlying reads in
the SAM files based on position. A multi-mapping index was defined for
each SNP as the proportion of underlying unique reads that mapped to
both parental genomes. Since homeo-SNPs arise from multi-mapped reads,
we hypothesized that the reads underlying private SNPs would display a
higher incidence of multi-mapping. Indeed, the mean multi-mapping index
was \textasciitilde{}2X higher for private SNPs than for shared SNPs for
both parents of both genera (Figure S1), suggesting that private SNPs
are enriched with homeo-SNPs.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\emph{Concordance of shared and private SNPs in P1 and P2 datasets}

P1 and P2 SNPs that are shared with the corresponding P1+P2 dataset have
much higher levels of concordance than private SNPs in the P1 and P2
datasets (Figure 5). In addition, these shared SNPs show the expected
increase in concordance with increasing sequence depth, similar to P1+P2
SNPs, whereas private SNPs show the unexpected increase in concordance
at low sequencing depth previously noted in Figure 3. This phenomenon
could be due to enrichment with homeo-SNPs, which are truly heterozygous
in all individuals but may appear as homozygous when sequencing depth is
limited. One limitation of our reduced representation library
preparation protocol is that alleles underlying a locus may have
different fragment sizes, leading to amplification bias (Davey et al.,
2011). In samples with very low sequencing depth, under-represented
reads from the larger fragment may be entirely absent, leading to higher
concordance at low depth in the private SNPs.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\emph{Alignment to a single parental genome is ineffective at resolving
population structure in the other parent}

Principal component analysis (PCA) was applied to P1, P2, and P1+P2
datasets to model population structure in the \emph{Juglans} population
of hybrids, which consists of three families derived from three maternal
parents and a single paternal parent (Figure 6). Whereas P1+P2 and P1
datasets were effective at resolving this population structure, the P2
dataset was not. To interpret this result, we note that the Juglans P2
dataset contains far fewer SNPs at MAF\textasciitilde{}0.25 (Figure 2),
the frequency we would expect for a diploid SNP showing a fixed
difference between the two common maternal parents. It is possible that
a mapping quality threshold less stringent than the one used in this
study (MAPQ\textgreater{}=20) might have been effective in retaining
more genetic signal from the non-aligned parent. Although the PCA plots
for P1 and P1+P2 look overall quite similar, the three samples with the
fewest sequencing reads (shown as blue triangles in Figure 6) drift
towards the origin in the P1 dataset but not the P1+P2 dataset,
suggesting the latter is more robust to low sequencing depth.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\emph{Future work and similarity with other methods}

Inbred or haploid genotypic datasets enjoy huge quality advantages over
heterozygous datasets at comparable levels of sequencing depth. This
study used a minimum depth threshold of 5 for P1 and P2 datasets, which
should theoretically lead to 93.75\% of truly heterozygous sites being
called correctly (assuming no amplification bias) and which actually
resulted in \textasciitilde{}80-90\% of the raw data being discarded
(Table 1). The luxury of relaxing or removing depth thresholds in inbred
datasets results in retention of much more data, and summarizing
heterozygosity by taxa or by SNP in inbred datasets simplifies the
removal of cross-contaminated DNA samples and homeo-SNPs respectively.
In this study, dual alignment of reads from interspecific hybrids to
both parental genomes (P1+P2) resulted in effectively inbred datasets
that enabled more rigorous quality control, displayed higher concordance
following downsampling, and provided more robust estimation of
population structure compared to standard alignment against a single
reference genome. Although this study used Beagle imputation for
purposes of comparing different alignment strategies, datasets resulting
from dual alignment could also be imputed using FSFHap, an imputation
method designed for inbred populations (Swarts et al., 2014), whereas P1
and P2 datasets could not. The practical conclusion of this study is
that dual alignment allows interspecific hybrids to be genotyped and
imputed as efficiently and inexpensively as inbreds.

The divergence between parental genomes in this study is estimated at 38
million years for \emph{Pistacia (P. atlantica} vs \emph{P.
integerrima)} (Xie et al., 2014) and 45 million years for \emph{Juglans
(J. microcarpa} vs \emph{J. regia)} (Stevens et al., 2018). This study
used 90 bp Illumina reads trimmed to 64 bp for speedier processing
through the TASSEL GBS pipeline (Glaubitz et al., 2014), of which 65\%
and 76\% mapped uniquely to the \emph{Pistacia} and \emph{Juglans} P1+P2
genomes respectively. Longer reads could be used to apply this strategy
to hybrids with lower divergence, and perhaps even hybrids between
heterotic groups within a species. Alternatively, strategies that make
use of a ``pan-genome'', including the Practical Haplotype Graph
(Bradbury et al., 2022), may achieve a similar result by including
enough representative reference contigs to ensure that all reads align
to a homologous (non-homeologous) sequence. The strategy described here
could also be applied to transcriptome data of hybrids to investigate
allele-specific or species-specific patterns of expression and
co-expression.

\textbf{Data Availability}

Raw sequence data for this project have been submitted to the NCBI SRA
under BioProject PRJNA909784. Twelve imputed VCF files representing two
genera, three alignment strategies, and two levels of downsampling are
included as supplemental material.

\textbf{Acknowledgments}

The author thanks Allison Krill for helpful discussion, and Steven Lee,
Kristina McCreery, and Ilean Tracy for assistance with tissue collection
and DNA extraction.

\textbf{Funding}

This work was supported in part by USDA NIFA-SCRI grant no.
2018-51181-28437, the California Walnut Board, and the California
Pistachio Research Board.

\textbf{Conflict of Interest}

The author declares no conflict of interest.

\textbf{References}

Ali, O.A., S.M. ORourke, S.J. Amish, M.H. Meek, G. Luikart, et al. 2016.
RAD Capture (Rapture): Flexible and Efficient Sequence-Based Genotyping.
Genetics 202(2): 389--400. doi: 10.1534/genetics.115.183665.

Alix, K., P.R. G\selectlanguage{ngerman}érard, T. Schwarzacher, and J.S. (Pat) Heslop-Harrison.
2017. Polyploidy and interspecific hybridization: partners for
adaptation, speciation and evolution in plants. Ann. Bot. 120(2):
183--194. doi: 10.1093/aob/mcx079.

Andrews, K.R., J.M. Good, M.R. Miller, G. Luikart, and P.A. Hohenlohe.
2016. Harnessing the power of RADseq for ecological and evolutionary
genomics. Nat. Rev. Genet. 17(2): 81--92. doi: 10.1038/nrg.2015.28.

Baird, N.A., P.D. Etter, T.S. Atwood, M.C. Currey, A.L. Shiver, et al.
2008. Rapid SNP Discovery and Genetic Mapping Using Sequenced RAD
Markers. PLoS ONE 3(10). doi: 10.1371/journal.pone.0003376.

Bradbury, P.J., T. Casstevens, S.E. Jensen, L.C. Johnson, Z.R. Miller,
et al. 2022. The Practical Haplotype Graph, a platform for storing and
using pangenomes for imputation. Bioinformatics 38(15): 3698--3702. doi:
10.1093/bioinformatics/btac410.

Browning, B.L., Y. Zhou, and S.R. Browning. 2018. A One-Penny Imputed
Genome from Next-Generation Reference Panels. Am. J. Hum. Genet. 103(3):
338--348. doi: 10.1016/j.ajhg.2018.07.015.

Bushnell, B. 2014. BBMap: A Fast, Accurate, Splice-Aware Aligner.
https://escholarship.org/uc/item/1h3515gn (accessed 6 December 2022).

Danecek, P., A. Auton, G. Abecasis, C.A. Albers, E. Banks, et al. 2011.
The variant call format and VCFtools. Bioinformatics 27(15): 2156--2158.
doi: 10.1093/bioinformatics/btr330.

Davey, J.W., P.A. Hohenlohe, P.D. Etter, J.Q. Boone, J.M. Catchen, et
al. 2011. Genome-wide genetic marker discovery and genotyping using
next-generation sequencing. Nat. Rev. Genet. 12(7): 499--510.

Elshire, R.J., J.C. Glaubitz, Q. Sun, J.A. Poland, K. Kawamoto, et al.
2011. A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High
Diversity Species. PLoS ONE 6(5): e19379. doi:
10.1371/journal.pone.0019379.

Ferguson, L., J.A. Poss, S.R. Grattan, C.M. Grieve, D. Wang, et al.
2002. Pistachio Rootstocks Influence Scion Growth and Ion Relations
under Salinity and Boron Stress. J. Am. Soc. Hortic. Sci. 127(2):
194--199.

Glaubitz, J.C., T.M. Casstevens, F. Lu, J. Harriman, R.J. Elshire, et
al. 2014. TASSEL-GBS: A High Capacity Genotyping by Sequencing Analysis
Pipeline (N.A. Tinker, editor). PLoS ONE 9(2): e90346. doi:
10.1371/journal.pone.0090346.

Gnirke, A., A. Melnikov, J. Maguire, P. Rogov, E.M. LeProust, et al.
2009. Solution hybrid selection with ultra-long oligonucleotides for
massively parallel targeted sequencing. Nat. Biotechnol. 27(2):
182--189. doi: 10.1038/nbt.1523.

Hulse-Kemp, A.M., J. Lemm, J. Plieske, H. Ashrafi, R. Buyyarapu, et al.
2015. Development of a 63K SNP Array for Cotton and High-Density Mapping
of Intraspecific and Interspecific Populations of Gossypium spp. G3
GenesGenomesGenetics 5(6): 1187--1209. doi: 10.1534/g3.115.018416.

Ledbetter, C.A., and M.S. Sisterson. 2008. Advanced generation
peach-almond hybrids as seedling rootstocks for almond: first year
growth and potential pollenizers for hybrid seed production. Euphytica
160(2): 259--266. doi: 10.1007/s10681-007-9569-1.

Li, H., and R. Durbin. 2009. Fast and accurate short read alignment with
Burrows--Wheeler transform. Bioinformatics 25(14): 1754--1760. doi:
10.1093/bioinformatics/btp324.

Marrano, A., M. Britton, P.A. Zaini, A.V. Zimin, R.E. Workman, et al.
2020. High-quality chromosome-scale assembly of the walnut (Juglans
regia L.) reference genome. GigaScience 9(5): giaa050. doi:
10.1093/gigascience/giaa050.

Palmer, W., E. Jacygrad, S. Sagayaradj, K. Cavanaugh, R. Han, et al.
2022. Genome assembly and association tests identify interacting loci
associated with vigor, precocity, and sex in interspecific pistachio
rootstocks. : 2022.06.28.498047. doi: 10.1101/2022.06.28.498047.

Poland, J.A., P.J. Brown, M.E. Sorrells, and J.-L. Jannink. 2012.
Development of High-Density Genetic Maps for Barley and Wheat Using a
Novel Two-Enzyme Genotyping-by-Sequencing Approach (T. Yin, editor).
PLoS ONE 7(2): e32253. doi: 10.1371/journal.pone.0032253.

Ramasamy, R.K., M.-C. Luo, C.A. Leslie, D. Velasco, N. Ott, et al. 2021.
Co-located quantitative trait loci mediate resistance to Agrobacterium
tumefaciens, Phytophthora cinnamomi, and P. pini in Juglans microcarpa ×
J. regia hybrids. Hortic. Res. 8(1): 1--11. doi:
10.1038/s41438-021-00546-7.

Stevens, K.A., K. Woeste, S. Chakraborty, M.W. Crepeau, C.A. Leslie, et
al. 2018. Genomic Variation Among and Within Six \emph{Juglans} Species.
G3amp58 GenesGenomesGenetics: g3.200030.2018. doi:
10.1534/g3.118.200030.

Swarts, K., H. Li, J.A. Romero Navarro, D. An, M.C. Romay, et al. 2014.
Novel Methods to Optimize Genotypic Imputation for Low-Coverage,
Next-Generation Sequence Data in Crop Plants. Plant Genome 7(3).
https://dl.sciencesocieties.org/publications/tpg/abstracts/7/3/plantgenome2014.05.0023
(accessed 4 January 2015).

Tinker, N.A., S. Chao, G.R. Lazo, R.E. Oliver, Y.-F. Huang, et al. 2014.
A SNP Genotyping Array for Hexaploid Oat. Plant Genome 7(3):
plantgenome2014.03.0010. doi: 10.3835/plantgenome2014.03.0010.

Warschefsky, E.J., L.L. Klein, M.H. Frank, D.H. Chitwood, J.P. Londo, et
al. 2015. Rootstocks: Diversity, Domestication, and Impacts on Shoot
Phenotypes. Trends Plant Sci. doi: 10.1016/j.tplants.2015.11.008.

Xie, L., Z.-Y. Yang, J. Wen, D.-Z. Li, and T. Yi. 2014. Biogeographic
history of Pistacia (Anacardiaceae), emphasizing the evolution of the
Madrean-Tethyan and the eastern Asian-Tethyan disjunctions. Mol.
Phylogenet. Evol. 77. doi: 10.1016/j.ympev.2014.04.006.

Zhu, T., L. Wang, F.M. You, J.C. Rodriguez, K.R. Deal, et al. 2019.
Sequencing a Juglans regia × J. microcarpa hybrid yields high-quality
genome assemblies of parental species. Hortic. Res. 6(1): 55. doi:
10.1038/s41438-019-0139-1.

\textbf{Supplemental Figures.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

